Embedded finance, aka embedded banking, is transforming the financial services distribution model. E-commerce companies, Point-of-Sale systems, ride-sharing apps, food ordering apps, and other digital financial service providers consider it a revenue opportunity. 

Is 2021 the Beginning of the Embedded Finance Era?

With over $7 trillion in revenue, embedded finance has generated a significant buzz in the FinTech market. Sadly, the financial services industry has not upgraded its core business model in years, and the COVID-19 pandemic has made the need even stronger than before.

While banks and insurance companies have spent exorbitant amounts of money digitizing their existing processes, it is high time that they invest fully in creating digital business models to recover the economic crisis.

Read more: The New Untapped Opportunities for FinTech Companies in the Coming Years 

Embedded finance helps businesses overcome digital adoption barriers and offer outstanding financial services to customers. While embedded finance will benefit the economy globally, its potential implication for the FinTech industry is massive.

Fingent’s FinTech application development services continuously provide financial service and technology innovations, valued by global financial services institutions. 

What is Embedded Finance?

Embedded finance is the amalgamation of a non-financial service provider with a finance service. It allows enterprises to create new revenue streams and reinvent the services they offer their customers. It is beneficial to both the enterprises as well as users. In most cases, it is easier to buy products from one single source instead of interacting with many other businesses over the day.

FinTech is already growing at a significant rate, and the pandemic has caused more people to use day trading platforms from lending sites to stocks.

As the world will start incorporating FinTech in their lives, embedded finance opportunities will increase in the future.

What are the opportunities for Embedded Finance?

The most significant advantage of embedded finance is that it streamlines financial processes. Previously, there was a gap between a consumer and the service provider or seller. So, the consumer would often approach a lender or a bank to bridge the gap. However, with embedded finance, the need for a third-party bank or lender is eliminated. Here are a few examples to understand how embedded finance can help you.

Read more: FinTech Innovation: What Is In Store?  

1. To make payments

For some consumers, paying with cash for a purchase hurts, making them reconsider a purchase. Embedded systems help eliminate this pain. A consumer using a mobile app with an embedded payment program can tap a few buttons and make a purchase instead of digging into their wallets for cash – for example, a ride-sharing app like Uber. So, when you book your ride, you don’t have to pay the driver cash or pull out your debit or credit card at the end. Instead, you complete the transaction in the app after you reach your destination. You can also use the embedded system to order your favorite cold brew or lip-smacking snack from Starbucks. The mobile app allows users to order and pay for their best-loved delicacies. Starbucks’ online ordering system also rewards customers with redeemable points for every purchase.

2. Lending

Before embedded finance, a person had to apply for a bank loan or open a credit card if he/she needed to borrow money. However, with an embedded system, a person can apply for and secure a loan at the time of purchase.

Klarna and AfterPay are examples of embedded lending. These programs split an online purchase into smaller monthly payments. For instance, a payment of $100 can be divided into four installments with $25 each.

Read more: FinTech: Safeguarding customer interest in the post-pandemic world 

3. Insurance

The need to consult an insurance agent or broker for purchasing an insurance policy is eliminated with embedded insurance programs. In the past, buying insurance was needed to buy a car or a house. Also, it was a completely separate part of the process. Some companies have now found ways to speed things up and increase their bottom line by embedding the action of applying for an insurance policy into making a necessary purchase.

For example, Tesla offers an insurance program that allows people to purchase an appropriate amount of coverage almost instantly. Additionally, the insurance available directly from Tesla costs less than a policy from a third-party insurance provider. 

4. Investment

Most people feel investing is a complicated process and prefer to stay out of it. However, embedded banking programs help simplify the investing program. 

For example, Acorns is a program that invests your spare change by rounding up purchases, thus making investing seamless and touch-free. It doesn’t require you to manually pay back the money since the app takes care of that. They adjust their portfolio according to the market, and so you don’t have to pay attention to the values of mutual funds or stocks.

How can enterprises use embedded finance or banking in their products or services?

Organizations can embed finance or banking in several ways. Even companies that are not in the FinTech industry are seeking ways to offer financial services. For instance, Shopify is offering lending services and bank accounts to companies. Organizations like Udaan and Grab have also started financial services like Udaan Credit and GrabPay.

In some cases, companies can act as connectors between financial services and non-financial businesses. For example, organizations can use a data transfer network by Plaid to offer financial products.

Another option for companies is that they can work with businesses that embed the required infrastructure into their products or services. With an increasing number of transactions and payment processing, the platform ecosystems can expand quickly, giving rise to the need for external financial services.

Read more: Technology in Finance: What to look out for in 2021?  

How is embedded finance beneficial to companies?

1. A new revenue system

Most customers show displeasure when redirected to multiple applications or experience a failed transaction due to timeout. The best resolution to this issue is to have a single unified flow in the customer journey. Customers would stay loyal to a brand if they have an easy-to-use eCommerce website.

Companies can charge a small fee as a commission on such transactions. It helps companies to have a new revenue opportunity without investing in bringing in new customers.

2. Increased hit rate/footfall

Embedded finance products can boost footfall if they can provide an overall improved experience. Given the cut-throat competition, customer loyalty can decline when a better product is launched in the market. Consumers will not hesitate to switch their allegiance to a competitor as long as they get what they need.

Companies can expect an increase in hit rate and better scope of converting users to potential customers with embedded finance products. If the transactions are smooth, the conversion rate will improve.

3. Use existing resources

Organizations need not worry about the expenses and resources needed to acquire new customers or procure high-level infrastructure. By including a financial angle to create an embedded product, you can modify the current systems.  

4. Improved customer experience

Embedded finance helps companies create a unified journey for their customers. Offering more services to the customers will eliminate their need to deal with a third-party vendor for completing their transactions. It will result in higher profits. The direct connection between the customer and the company will help improve the customer experience significantly.

Read more: Digital Transformation in Financial Services: All You Need to Know  

How will embedded finance change the future of the FinTech landscape?

With the evolving nature of technologies, embedded finance will persist due to its customizable nature. It will give rise to new opportunities and reduce the gap between various industries and their interactions. 

Companies must be open to collaborating to build a bigger market, survive, and stay ahead of the competition. Software solutions providers and technology companies like Fingent play a crucial role in boosting the financial services landscape. Contact us to know more about our FinTech software development services and solutions. 

Technology in Finance: An Overview of the 2021 Landscape 

Technology in finance, along with evolving consumer behavior and regulations, are transforming the financial services industry. The COVID-19 pandemic is accelerating the industry’s focus on digital offerings. Government payment schemes, emergency loans, and personal finance management are the need of the hour.

With several bank branches shut and long waiting hours for phone assistance, financial institutions are forced to invest in better IT infrastructure, relevant automation, and technology in finance to deal with the growing consumer demands.

Read more: The impact and significance of digital transformation in financial services. 

A study of financial institutions (FIs) by ISMG and OneSpan in North America revealed that providing customers with a top-notch experience is their main priority. 49% of the respondents feel that legacy and manual ID verification are the biggest obstacles to digital opening for FIs, while 35% found that knowledge-based authentication tools were obstacles to onboarding. 

Some general FinTech statistics worth knowing are:

• By 2022, the global financial sector is expected to be worth USD 26.5 trillion with a CAGR of 6%.
• 49% of banks and 60% of credit unions in the US believe that FinTech partnerships are worth it.
• Digital payment is one of the most significant FinTech products and holds about 25% of the FinTech market.

That said, many banking and financial institutions are adopting the latest technologies such as artificial intelligence, blockchain, etc., into their operations to benefit their customers, stay competitive, and improve business growth.

Read more: FinTech: Safeguarding customer interest in the post-pandemic world 

Here are the top five technologies that transform the financial services industry currently. 

1. Artificial Intelligence

The most crucial advantage of Artificial Intelligence in the finance industry is cost savings, which is anticipated to be worth $447 billion by 2023.

AI systems are a game-changer for the finance industry as they can examine vast amounts of data and find patterns and trends that people may miss, and even predict future trends. AI technology makes it possible to automate processes and manage tasks such as comprehending new rules and regulations or generating personalized financial reports for individuals. For example, IBM’s Watson is capable of understanding complex regulations, including reporting of markets’ requirements in the Financial Instruments Directive and the Home Mortgage Disclosure Act.

Chatbots in banking are helping automate simple tasks such as opening a new account or transferring money between accounts and are proving to be a great money-saving tool.

Many financial institutions such as Bank of America and JP Morgan Chase use AI to streamline customer service. Additionally, AI facilitates mobile banking that allows 24/7 access to customers to conduct banking operations. AI is also helping financial institutions boost security and detect and prevent fraud. 

2. Blockchain

Blockchain is a promising technology that will impact financial systems significantly. Blockchain technology is inspiring to create several P2P (peer to peer) online financing platforms that help monetary interactions happen in a more decentralized way. Blockchain technology can improve existing systems and processes and create cryptocurrencies.

Five typical applications of blockchain include:

• Make cross-border transaction processes faster, more accurate, and less expensive
• Banks can leverage trade finance to create smart contracts between participants, increasing transparency and efficiency
• Clearing and settlement procedures
• Protect against fraud and speed up the verification process with blockchain-enabled IDs
• Credit-reporting

3. RegTech

RegTech is a regulatory technology that uses cloud computing technology through SaaS (software-as-a-service) to help businesses comply with regulations efficiently and lower costs.

The various areas of RegTech intervention are:

• Data management
• Reframing regulations and implementing new governances
• Real-time reporting
• Data- analytics and decision
• Fraud and risk management

Non-compliance with mandatory government rules leads to fines and crisis. So most FIs want to do everything in their power to avoid non-compliance.

The need for RegTech solutions is growing as FIs grapple to stay compliant with new and existing regulations. RegTech solutions will create a layer that companies will rely upon significantly. Its high accuracy, single dashboard, data analytics, alerts, and insights will help companies optimize resources allocated to compliance and achieve better results.

4. Machine learning 

Similar to AI, machine learning helps create a marketing campaign around the consumer. It enables you to understand what kind of services will attract your target market. For example, how people find a financial website, what page they clicked, and what services they need.

Machine learning algorithms and their capability for sentiment analysis will impact trading significantly in the future. It involves using enormous volumes of unstructured data such as photos, video transcriptions, social media posts, presentations, webpages, blogs, articles, and business documents to understand the market sentiment.

Sentiment analysis will transform the future financial markets, and many believe that machine learning will be central to developments.

5. Big Data

According to the IDC Semiannual Big Data and Analytics Spending Guide, currently, banking is one of the top investors in big data and business analytics solutions. Credit card transactions, ATM withdrawals, credit scores, etc., generate massive amounts of data. Deriving actionable insights from this data is crucial to optimize financial processes and make effective business decisions. It will increase the competency of financial institutions in the future. 

Big data can help FIs learn more about customers and make business decisions in real-time. Big data analysis allows FIs to identify market trends and streamline internal processes and reduce risks.

Read more: FinTech Innovation: What Is In Store? 

The Future of FinTech Adoption

82% of traditional financial organizations plan to collaborate with FinTech companies in the next five years as they fear losing out. 88% of established FIs believe that they may lose to standalone FinTech companies in the next five years if they fail to adopt FinTech innovation.

Financial companies will have to work towards providing a seamless digital experience for their consumers. To avoid the risk of losing out in the market, many FinTech startups, incumbent financial institutions, and technology companies are entering into new partnerships. 

Read more: The New Untapped Opportunities for FinTech Companies in the Coming Years 

Technology in finance is no different than other disruptive technologies across various industries. It would be wise for even small businesses to consider FinTech as an investment for the future. Fingent has developed end-to-end disruptive technologies and innovative FinTech solutions that will help your business thrive and stay relevant. Contact us for more details.

The inevitable role of FinTech in improving your financial systems and outcomes

The COVID-19 outbreak has affected every aspect of the economy including financial technology or FinTech. Postponed events and conferences mark missed opportunities for FinTech companies, which could have been a great time to build relationships and focus on new businesses. As investors and customers retreat to more cautious positions, FinTech companies may find fundraising a challenge. Those who seek consumer investments are hit harder. Consumers may be reluctant to invest during such volatile times. Even those consumers who are relatively insulated from economic fallout may choose to invest in safer options for the present. FinTech innovations can improve the efficiency of the financial system and financial outcomes for their customers. This article will discuss how FinTech can safeguard customers’ interest in the post-pandemic world. 

What is FinTech?

FinTech is a combination of Finance and Technology. It is used to describe new technology that can improve and automate the use and delivery of financial services. It also enables people to live upgraded lives through innovation. FinTech includes many sectors such as fundraising, education, retail banking, and more. It plays a major role in the development and usage of cryptocurrencies. FinTech also covers various day-to-day financial activities including money transfers, check deposits, and investment management.

Read more: FinTech Innovation: What Is In Store?    

Why protect customer interest?

Customers are the primary source of growth, so they must be handled with the respect they deserve. Any product or service which is customer-centric offers the potential to attract and retain customers. Since FinTech provides advantages of speed and convenience, customers are looking at FinTech as a viable alternative. People want streamlined services with applications that are easy to adapt to. Hence, FinTech companies are outlining measures to make their services less complex and more transparent. They are focusing on creating better digital processes that their customers can personalize easily.  

Customers’ convenience and requirements are paramount for FinTech start-ups. To that end, they are designing products and solutions to ensure customer satisfaction. Delivering a top-notch customer experience is the goal of FinTech companies globally.

Measures to protect customer interest 

Here are some cutting-edge technologies that are protecting customer interest now and into the future:

1. IT foundation for better customer experience

FinTech startups are usually smaller in size and have a technological edge. They have a fresh canvas, allowing them to migrate easily from legacy technologies. The younger digital-first audience is attracted to their services. Larger FinTech enterprises must adopt a new IT foundation with modern technologies. Currently, FinTech customers prefer startups over established brands because they can reap the rewards in the form of better digital experiences. Though startups have a technological advantage, they must continue to focus on their capital reserves to make it through these unprecedented times. 

2. Digital communication tools 

The FinTech sector is based on understanding the needs of their customers. It is crucial for these companies to strategize the manner in which service providers communicate with their customers. This gets customers locked onto their services with relative ease. Communication through online media or through the content on your site can draw in new leads and build customer trust. When customer interest is protected, they will most likely return to you. In turn, they will recommend the service to their relatives and friends. These parameters are crucial if you want to keep your business afloat. 

3. Embrace digital transformation

While your staff may be susceptible to coronavirus, technologies like ML and AI are immune. The financial services system must address customers’ demands swiftly and efficiently. Smart devices and the integration of artificial intelligence are a great way to achieve this. Virtual assistants and chatbots can deliver a customized experience to your customers. They perform all the activities that are usually done by customer service personnel and other executives. However, these digital solutions are faster and reflect sophistication. Digital transformation provides holistic 24/7 monitoring and automated remediation. 

Read more: Artificial Intelligence In Investment Management: What To Expect 

4. Digital banking 

Previously, a customer’s confidence in a financial company depended mostly on physical infrastructure. However, COVID has changed that momentously! The new generation banking system is going all-digital to reach mobile-first customers. Digital-only banks do not need sophisticated infrastructure or higher human resource management. Digital banks are able to deliver cost-effective, robust services that match the high standards set by traditional banks.

5. P2P Transactions

P2P digital payment is quickly gaining popularity. Customers are adopting such technologies for daily use. P2P eliminates the middle layer and drastically reduces transaction costs. Digital transactions help FinTech enterprises expand their footprints and customer base. 

6. Security and privacy

FinTech is an industry where the risk of financial crime is high. It is vital for FinTech companies to think over customer security while designing their consumer experience. Apparent security measures make customers feel comfortable. Customers expect rigid security from FinTech solutions along with reliability and FinTech is practicing stringent security measures to beat the competition. They are making visible efforts to handle customer data with care. To gain the attention of your customers you can make your privacy policies visible enough on your website or app. Remember, it can reflect on the confidence a company has in its security measures.

Read more: Artificial Intelligence and Machine Learning: The Cyber Security Heroes Of FinTech 

Changing for the better

It may be difficult to predict how the payments landscape will emerge in the next few years and what will be the long-term impacts on the FinTech industry. Nevertheless, it is likely to witness a transformation that can dwarf what has been achieved thus far. At such times, it is important to gain the confidence of your customers to retain them and enjoy their loyalty. 

Thus far, FinTech has only been in the shadows as it were, but now it has found a home in the innovation economy globally. Millennials are more reliant on their smart devices to accomplish their daily tasks. They want the world and its conveniences at their fingertips anywhere and anytime. Given that, perhaps the future might see more interesting innovations in customer experience. 

Let’s look at some opportunities for FinTech in the future:

• Companies with remote workforces are better positioned to thrive during and after this difficult period.
• FinTech gives an impetus for greater adoption of contactless money transactions.
• FinTech companies are well-positioned to find new ways to incorporate better digital solutions.

In order to capitalize on all these opportunities, you will need a technology partner to help guide you through the latest innovations. Give us a call and let’s discuss how Fingent can help you guide your business and customers to success in the post-pandemic world… 

Transforming Businesses with RPA- Leading Use Cases in HR and Banking

Various organizations use RPA tools to automate simple to complex tasks and perform them with minimal or no human intervention. 

From an IT perspective, you tend to bucket all RPA uses cases into data integration or testing. However, from a business perspective, you need to find out how to get a better time to value and how to overcome obstacles that hinder the business value. Then you can determine use cases that fit into this characterization. 

For example, you want to roll out a change in your business process, and need integration into another system. You can do that in two ways:

• either through APIs and get into the IT changed management routine, 
• or by using RPA to drive interfaces without an API and get the change rolled out in weeks instead of months. 

So, time to value is the calculation that businesses need to do, and check whether the change is worth it. 

Read more: What Makes a Business Process Apt for Automation

Suppose you have to perform tasks that are very repetitive in nature – like filling in excel forms, web forms, things like visual basic or word with data which you already have access to, or which you need to aggregate from various systems. Here you can have an RPA bot to pull that data or even push out that data to multiple systems. You won’t have to rekey that information manually. You can always use an RPA bot to do that in an automated fashion. In both these cases, you can write integrations or you can have a system do it for you. 

RPA gives you a way to configure that behavior rather than write a code for it. In other words, RPA use cases need to be data-intensive, rule-driven, and repetitive. The drivers almost always tend to be time to value, time to market, and so on.

Now that you’ve understood where to use RPA in your business, let’s have a look at some of the use cases. 

RPA Use Cases in HR 

According to UiPath, 40% of your HR professionals’ time can be reclaimed using RPA. Robotic Process Automation can be combined with your existing HR systems like SAP or Workday that allows you to create digital process automation with ease. Here are the two key HR areas where automation leads to transformation.

1. Payroll: 

Payroll operations consist of a large number of repetitive, rule-based tasks with activities like data collection, calculations, and scheduling tasks. Payroll workers have to collect data from various departments or units in different formats. The next step is data validation and entering that information into other applications. All these tasks are prone to error. 

These activities can be automated using RPA technology since all the data that payroll staff deals with is structured. RPA can make payroll more organized without using expensive software. 

The benefits of RPA in payroll are improved accuracy, lower costs due to reduced manual labor and data security. Since the number of menial, time-consuming tasks performed by employees is reduced, they can focus on tasks with higher strategic value.

2. Onboarding and offboarding:  

Every time you get a new employee, the candidate’s details have to be uploaded to all systems that you use. They may need a Windows account, access to your time reporting tool, email addresses, IT equipment, and so on. If someone from the HR team manually enters all this data they would be stuck in mundane tasks. Instead, you can have a script doing these repetitive tasks. With RPA, you can automate the entire onboarding procedure since the process is the same for every new employee. 

Employee exits too, have to be managed consistently. Manual processing makes these tasks error-prone and may raise auditory concerns. If RPA is implemented in this case, the bot analyzes the incident to find out which tasks need to be executed. It notifies the IT team to terminate access and recover the equipment, terminates the employee from the HCM, revokes system access, generates exit documents, and processes final payments. 

Read more: Jaw-dropping Facts about Robotic Process Automation

RPA Use Cases in Banking

A slow economy and rising customer expectations have caused banks to look for cost optimization methods. The back-end processing activities in the banking sector consist of tasks that are rule-driven, repetitive, labor-intensive, and high in volume. RPA technology can help to automate these processes, thus eliminating the need for human intervention. Here are the two major banking functions that can be automated for improved results.

1. Loan application processing: 

The processing of loan applications is a tedious process. For document verification, employees need to manually verify different documents and associated information and then organize all data into a single file. Very often, employees get stuck in this task and spend too much time on it. RPA employed in this procedure can automate the whole process by opening different web portals and validating the information. The bot then initiates an email to the employee for a final decision. Thus, the bot helps to save valuable time and improves the time to client response.

2. Account opening: 

The account opening process is cumbersome, time-consuming, and prone to errors. RPA can help speed up this process and make it more accurate. Bots draw out information from forms and enter it into separate host applications. Thus RPA eliminates errors and improves the quality of data in the system.

Read more: How Robotic Process Automation Simplifies Business Operations

RPA tools have the potential to help various industries improve efficiency, drive faster operations, and reduce costs than most automation techniques. RPA is gaining popularity as enterprises try to counter competition, increase productivity, and meet customer expectations. Early adopters of RPA have reaped its benefits and its high time that you did too. Get in touch with our experts to learn more about how RPA can simplify your business operations. 

What are preconfigured solutions?

SAP Preconfigured solutions are implementations featuring standard functionality or common patterns. Since they include common enterprise features such as supply chain, finance, and inventory management, preconfigured solutions can be deployed rapidly. In simple terms, SAP preconfigured solutions are simple, ready-to-consume solutions that adapt to your business needs.

SAP Rapid Deployment Solutions allows the development of preconfigured solutions for specific functional areas, complete with pre-built reports, preconfigured content, and predefined best practices. Such suites enable enterprises to deploy software easily, with accelerated implementation times, and predictability in cost and scope.

Each preconfigured solution is a complete, end-to-end implementation. Enterprises may adopt it as such, or use it as a base for developing a full-blown customized solution. Open source solutions allow enterprises to download the source code and customize the solution.

Traditional preconfigured solutions came on-premise. The SAP HANA Enterprise Cloud heralds the cloud version of preconfigured solutions.

Related Reading: What is Business Process Expertise in SAP and Why You Need it

How preconfigured solutions enhance industry operations?

Preconfigured solutions spare enterprises from “rediscovering the wheel.” When solutions exist for standard requirements, it avoids the wastage in development efforts. Preconfigured solutions save time, trial-and-error, and labor. The in-built accelerators allow rapid implementation and configuration. The shortened time to market delivers a competitive advantage to firms.

A preconfigured solution offers a working model with glitches removed. These solutions connect key functional areas, to streamline operations and improve communication flow across departments. It automates routine and repetitive functions and offers well-structured content that improves visibility into key fields.

SAP offers built-in support for industry best practices. Solutions developed using SAP combines the latest technology, industry best practices, and processes in a single package.

Developers using SAP to build preconfigured solutions can adopt a modular and scalable approach. They mix modules to create targeted solutions such as CRM, field management suite, ERP and more. Clients may choose the most relevant reporting tools for their reports using SAP Business Intelligence Adoption rapid-deployment solution. SAP Predictive Analytics Content Adoption rapid-deployment solution helps to implement a predefined predictive analysis use case.

Transforming various industry verticals

Preconfigured solutions deliver efficiency improvements in several industries and verticals. Five of them are mentioned below:

1. Real Estate

Unstandardized information, inefficient processes, and weak communications often affect the real estate industry. Preconfigured solutions enable enterprises to streamline data and workflows. 

Developing preconfigured solutions for key functional areas of the real estate industry allows business owners to access information in real-time, prevent opportunities from slipping through the cracks, and remain in control of the business. Here are a few key functional areas where pre-configured solutions can come to the rescue of real estate enterprises:

• Document management with powerful search and sort functionality.
• Process automation functions to streamline operations.
• Contact maintenance to keep track of customers and upcoming tasks.
• Reporting and analytics to generate and share data and other critical information.

2. Supply Chain and Logistics

Preconfigured solutions make the complex supply chain relationships simple and easy to handle. SAP’s Model Company for supply chain planning offers a ready-to-run, comprehensive solution, complete with relevant business content, accelerators, and enablement services.

The suite co-opts:

• Demand planning to manage demand streams and increase productivity.
• Response and supply planning.
• Supply and allocation planning.
• Deployment planning.
• Sales and Operations planning.

The inbound and outbound integrations with SAP S/4HANA ensures seamless integration with other functional areas, such as sales. Businesses gain improved visibility to the upstream and downstream business value chains and can visualize the entire supply chain network. The preconfigured solution aligns demand and supply plans with financial targets and key performance indicators.

Related Reading: Seamlessly migrate to S/4HANA with these tools and methodologies

3. Healthcare

The healthcare industry is under pressure to innovate while keeping up with regulations. Preconfigured solutions automate regulatory concerns and compliance. Ready-to-run process models and scenarios enforce standardization with tight quality controls. It improves efficiency in research and development. Accelerators support rapid design and implementation.

The most popular modules of SAP’s preconfigured solutions in healthcare are meant for:

• Research and development
• Production
• Regulation
• Quality and traceability
• Logistics and transport
• Purchases and provisioning
• Marketing and commercial

4. Sales and Marketing

SAP preconfigured solutions infuse efficiency to customer-facing functions and increase customer satisfaction. Its most popular implementations in the sales and marketing domain include:

• Predicting customer demand based on real-time data.
• Tracking sales history.
• Automating sales operations. The solution tracks all processes, from ordering of raw materials till the delivery, and takes care of auditing, notifications, and reporting.

5. Financial Services

The SAP Model Company solution for Financial Services offers a ready-to-run, comprehensive solution to support the core financial processes, including:

• General accounting
• Cash and treasury functions
• Planning and forecasting

These preconfigured solutions come with relevant business content for a specific use-case, accelerator, and enablement service. These solutions enable gap analysis, address resource bottlenecks, and support lean implementations.

Today’s enterprises seek efficiency improvements and cost savings without compromising quality. Fingent uses SAP to build pre-configured solutions that enable enterprises to realize such goals. Implementing these custom-built pre-configured solutions helps you transform your processes across various business areas and improve your customer relationships. 

Contact us to get a free consultation with our SAP Experts. 

Knowledge Representation Models in Artificial Intelligence  

Knowledge representation plays a crucial role in artificial intelligence. It has to do with the ‘thinking’ of AI systems and contributes to its intelligent behavior. Knowledge Representation is a radical and new approach in AI that is changing the world. Let’s look into what it is and its applications. 

Understanding Knowledge Representation and its Use

Knowledge Representation is a field of artificial intelligence that is concerned with presenting real-world information in a form that the computer can ‘understand’ and use to ‘solve’ real-life problems or ‘handle’ real-life tasks.

The ability of machines to think and act like humans such as understanding, interpreting and reasoning constitute knowledge representation. It is related to designing agents that can think and ensure that such thinking can constructively contribute to the agent’s behavior. 

In simple words, knowledge representation allows machines to behave like humans by empowering an AI machine to learn from available information, experience or experts. However, it is important to choose the right type of knowledge representation if you want to ensure business success with AI. 

Four Fundamental Types of Knowledge Representation

In artificial intelligence, knowledge can be represented in various ways depending on the structure of the knowledge or the perspective of the designer or even the type of internal structure used. An effective knowledge representation should be rich enough to include the knowledge required to solve the problem. It should be natural, compact and maintainable. 

Related Reading: 6 Ways Artificial Intelligence Is Driving Decision Making

Here are the four fundamental types of knowledge representation techniques: 

1. Logical Representation

Knowledge and logical reasoning play a huge role in artificial intelligence. However, you often require more than just general and powerful methods to ensure intelligent behavior. Formal logic is the most helpful tool in this area. It is a language with unambiguous representation guided by certain concrete rules. Knowledge representation relies heavily not so much on what logic is used but the method of logic used to understand or decode knowledge.

It allows designers to lay down certain vital communication rules to give and acquire information from agents with minimum errors in communication. Different rules of logic allow you to represent different things resulting in an efficient inference. Hence, the knowledge acquired by logical agents will be definite which means it will either be true or false. 

Although working with logical representation is challenging, it forms the basis for programming languages and enables you to construct logical reasoning.

2. Semantic Network

A semantic network allows you to store knowledge in the form of a graphic network with nodes and arcs representing objects and their relationships. It could represent physical objects or concepts or even situations. A semantic network is generally used to represent data or reveal structure. It is also used to support conceptual editing and navigation. 

A semantic network is simple and easy to implement and understand. It is more natural than logical representation. It allows you to categorize objects in various forms and then link those objects. It also has greater expressiveness than logic representation. 

Related Reading: Understanding The Different Types Of Artificial Intelligence

3. Frame Representation

A frame is a collection of attributes and its associated values, which describes an entity in the real world. It is a record like structure consisting of slots and its values. Slots could be of varying sizes and types.  These slots have names and values. Or they could have subfields named as facets. They allow you to put constraints on the frames. 

There is no restraint or limit on the value of facets a slot could have, or the number of facets a slot could have or the number of slots a frame could have. Since a single frame is not very useful, building a frame system by collecting frames that are connected to each other will be more beneficial. It is flexible and can be used by various AI applications. 

4. Production Rules

Production rule-based representation has many properties essential for knowledge representation. It consists of production rules, working memory, and recognize-act-cycle. It is also called condition-action rules. According to the current database, if the condition of a rule is true, the action associated with the rule is performed. 

Although production rules lack precise semantics for the rules and are not always efficient, the rules lead to a higher degree of modularity. And it is the most expressive knowledge representation system. 

Gain the Benefits of Knowledge Representation

Used properly, knowledge representation enables artificial intelligence systems to function with near-human intelligence, even handling tasks that require a huge amount of knowledge. The increasing use of natural language also makes it human-like in its responses. Making the right choice in the type of knowledge representation you must incorporate is crucial and will ensure that you get the best out of your artificial intelligence system. If you need help with this, we’re here. Please reach out to us. 

How to Solve Accounting Challenges in Business with Augmented Intelligence

The challenges faced by finance and accounting teams are like the underwater icebergs that can crash a huge ship. The Titanic sank because of poor decision-making. Likewise, weak financial decisions can affect your business. This blog will help your finance and accounting teams to identify the hidden challenges and provide insights on how to use Augmented Intelligence to overcome complex business challenges effectively. 

5 Reasons Why Augmented Intelligence Is Gaining Importance 

Many businesses are embracing Augmented Intelligence because;

• Enormous volumes of data can be processed quickly and efficiently with Augmented Intelligence.
• Accounting tasks such as audits, payrolls, taxes, and banking can be automated using Augmented Intelligence.
• Due to its ability to continuously learn, Augmented Intelligence can constantly improve efficiency while eliminating the risk of human error.
• It enables humans to make crucial decisions without bias by providing fair information and recommendations.
• Tedious tasks such as bookkeeping can be automated and streamlined.

Top 4 Solutions Offered by Augmented Intelligence 

Challenge 1: Protecting the business from fraud

According to the 2018 global fraud and identity report, 63% of businesses still continue to experience the same number or more fraud losses than the preceding year. And only 54% are ‘somewhat confident’ in their ability to detect fraudulent activity. The wide variety of fraud types and the enormity of the work involved in reviewing the data manually or by rule-based systems can make the detection and prevention of fraud a huge challenge.

Solution: 

With the help of Augmented Intelligence, large transactions can be analyzed in real-time which helps in detecting fraud. Since Augmented Intelligence can even categorize the score of fraudulent activity, investigators are able to prioritize their work effectively. Once the fraud is detected, Augmented Intelligence allows you to reject the transaction outright. Since Augmented Intelligence continues to learn from past data, it can learn from investigators’ reviews and understand how to discern patterns that lead to fraudulent activities.

Related Reading: Artificial Intelligence and Machine Learning: The Cyber Security Heroes Of FinTech

Challenge 2: Risk Assessment

While evaluating potential risks in lending money or providing credit, businesses could end up denying credit without assessing their current situation using traditional methods. Worse yet, they could end up approving credit to churners who could affect profits. The organization might also face the challenge of explaining to the consumer the reason for denying them credit.  

Solution: 

Augmented Intelligence helps you assess your customers’ current income and recent credit history based on the enormous data that is available at hand. This allows for a more realistic and accurate assessment of each borrower. Such kind of assessment allows financial firms to make more individualized decisions. Besides, Augmented Intelligence can provide reason codes which would explain the important aspects involved in credit decisions, making it easier to provide reasons why credit is being denied.

Challenge 3: Trading and Investment

According to a 2018 survey conducted in the US, 70% of millennials use mobile banking in the US alone. And this figure is steadily increasing all over the world. Businesses cannot function without mobile applications. It has become a channel of interaction with customers who would like to review transactions, pay bills and find customer service. Failed interactions would translate into increased customer churn, lost transactions and even lost revenues.

Solution: 

Augmented Intelligence can assist your business in detecting anomalies in transaction volume by identifying the triggers for such anomalies. Based on previous data patterns, the system can look at expected data volumes which can then be compared with real-time transaction values. This will help in your decision-making process because it clearly and quickly indicates the highs and lows of a transaction by suggesting solutions that meet each individual demand.

Challenge 4: Combating Money Laundering

It is estimated that the amount of money laundered globally in one year is 2 – 5% of the global GDP! And this seems to be increasing at an alarming rate. To combat money laundering, extensive investigations must be performed by the finance and accounting teams. 

Solution: 

Augmented Intelligence can detect suspicious and complex transactions and raise a red flag on such transactions so investigators can further examine them. Augmented Intelligence can learn from each experience and more effectively safeguard your firm.

Related Reading: The Future Of Communication and Security Using Augmented Reality

Discover New Growth Opportunities by Applying Augmented Intelligence

Augmented Intelligence can help finance and accounting teams reduce costs, improve operations, increase consumer satisfaction and reduce the time taken for various processes by 80-90%. It can also reshape your entire organization from internal operations to treasury services. It can assess the available unstructured content and help your business unlock valuable insights from them. This enables smarter decision making, which in turn helps in the growth of your business. 

When your business adopts Augmented Intelligence as part of your methodology, it gives your customers benefits that will lead to loyalty and growth. Fingent has been helping many clients achieve this, and we can help you too. Give us a call and let’s discuss. 

Robotic Process Automation Reflecting The Banking, Insurance & Logistics Industries

Robotic Process Automation is the fastest-growing segment of the global software market. Using this technology, companies can fast-track their digital transformation initiatives. Although RPA is useful in all industries, the biggest adopters of this technology are banks, insurance companies, and logistics. These companies traditionally have numerous legacy systems and choose RPA solutions to enhance or replace manual IT processes. This article discusses how Robotic Process Automation is revolutionizing the banking, insurance, and logistics industries. First, let’s consider what is Robotic Process Automation.

What is Robotic Process Automation?

Robots have fascinated humans for a very long time. From the futuristic robot Maria from the movie Metropolis in 1927 to this day of AI sci-fi, the possibility with robots is a topic of endless discussion. Interestingly, though, this word “robot” in Robotic Process Automation (RPA) does not involve a physical or mechanical robot. Instead, it is a software running on a virtual or physical machine. Aaron Bultman, director of Product at Nintex simplified the expression saying, “RPA is a form of business process automation that allows anyone to define a set of instructions for a robot or ‘bot’ to perform.” 

How to accelerate your business growth with Robotic Process Automation

According to Gartner, RPA grew by 63% in 2018 and continues to be one of the fast-growing technologies in many industries! RPA lets companies automate current tasks as if a real person was doing them across systems and applications. It interacts with each system just as a human user would interact. This does not require complex system integration. These functional RPAs are virtual workers and execute rule-based information processes, enhancing efficiency and accuracy. Additionally, it is easy to model and deploy. Let us consider the three specific industries where RPA is most beneficial.

RPA in Banking

The banking industry is completely process-oriented. Every activity is done meticulously to avoid errors in processing. The repetitive nature of the job and the high probability of human error could cause mistakes that might prove very costly. Therefore, in the recent past, leading banking institutions have started using RPA to minimize errors. According to a report by KPMG, RPA will perform almost 75% of the existing offshore jobs, which could save operational cost. 

Scalable Benefits of RPA in the Banking Industry

• Mortgage Lending. RPA can enable the banking industry to meet loan quality and cost concerns head-on. Automation of repetitive and time-consuming manual tasks will free up your team to focus on the more important details of loan applications. It speeds up the work, increases revenue. enhances customer experience, reduces operational costs, decreases risk and improves compliance. 

• Compliance and Risk Management. It enables the banking industry to automatically integrate and aggregate compliance information into business processes, streamlining the required work while reducing expenses. RPA eliminates the need for manual regulatory monitoring and data collection. It can empower risk management and compliance teams. It enables banking industries to avoid costly fines and damages to reputation. It provides accurate and complete information.

• Customer Service and Support. RPA ensures the longevity and loyalty of your customer relationship and the future of your bank. It helps you engage customers in real-time. It automates customer service activities so the customer is not waiting endlessly. It increases the productivity and efficiency of the team.  

Related Reading: Check out these 5 simple hacks on using banking mobile apps safely. 

RPA in Insurance

An insurance company receives hundreds of claim requests. Validating each request and handling them is not only a herculean task but also a painstakingly slow manual process. The entire process of the claim takes several days. Because this is a costly and time-consuming process, the company risks losing customers. To prevent further damages, insurance companies are now relying on RPA to help them improve operational excellence and reduce costs.

Scalable Benefits of RPA in the Insurance Industry

• Improves customer service because RPA effectively reduces the turnaround time in resolving customer issues.
• Processes and workflows could be tracked and recorded at each phase. This reduces staff workloads and improves process efficiency.
• Reduces the processing time by 40-80%.  This frees up the team to focus on more important activities such as acquiring new clients.
• Reduces errors made during data entry significantly.
• Speeds up and organizes the processing of claims through a systematic underwriting process.
• Based on the demand, intelligent bots can scale up or down, which delivers consistency in service and operational efficiencies.
• Improves audibility and operational risk management due to the accuracy level of RPA.
• The time for the cancellation process could be reduced by one-third.

Related Reading: Here’s how machine learning is accelerating paperless offices for legal firms. 

RPA in Logistics

The logistics industry depends on several processes to facilitate the proper distribution of products, materials, and services from B2B or B2C. The need for an intelligent logistics system increases as the industry along with competition advances every day. Here’s how the RPA can contribute to the logistics industry:

Scalable Benefits of RPA in Logistics.

• Better data management and customer service.  Negative experiences and a multitude of errors with logistics providers are causing customers to lose their trust in online shopping. With the benefits of automation and digitalization brought about by RPA, logistics providers no-longer need to rely on huge amounts of paperwork, which was the underlying cause for errors and poor customer service. Logistics companies are enabled to manage real-time monitoring of flow and resources, availability, costs, staffing, transportation, suppliers and so on more efficiently. 
• Improved work safety for employees. Through the use of automated machines in unsafe environments and the reduction of repetitive stress in manual tasks, work injury has been dropping steadily with the aid of RPA. This results in savings in terms of injury compensation and loss in reputation. It can also increase productivity, improve employee satisfaction and loyalty, and increase their efficiency.
• Improved efficiency and precision. RPA can help logistics companies easily manage the supply chain processes more efficiently. The anticipatory logistics system helps companies gauge the demand from their customers and adjust their production volume accordingly. It can help in managing and analyzing huge amounts of data within seconds, resulting in fewer human errors, faster deliveries, and fewer errors in delivery.
• Cost reduction. Fewer errors from human decision-making result in cost savings for logistics companies. Also, RPA reduces the need for human workers, which results in fewer paychecks and more profits. Where workers are still needed, it can raise their productivity, margins to a whole new level. It improves customer satisfaction and creates a safer work environment for the workers.

Related Reading: Know more about how AI is reshaping the supply chain and logistics industry.

Empower Your Business With RPA 

According to Global Market Insights Inc., the RPA market is expected to reach $5 billion by 2024. It is increasing capabilities and improving performance while reducing costs in several industries. Fingent Technologies has been one of the top software companies empowering industries globally with robotic process automation capabilities. Give us a call and let us discuss how we can transform your business with RPA.

How Machine Learning Systems Detect And Prevent Frauds Without Affecting Your Customers

There is nothing more fearful than imbalanced data, especially when dealing with various payment channels like credit and debit cards in banks and other financial organizations. With the wide increase of different payment mediums, businesses are finding it difficult to authenticate transactions. But Machine Learning has been a viable solution to detect fraudsters.

Machine Learning can be referred to as the ability of machines to learn data with the help of human intelligence as well. According to the latest report by Gartner, by 2022, more than nearly half the data and analytics services/ tasks will be done by machines.

Related Reading: Read on to learn how machine learning can help boost customer experience. 

Machine Learning In Making Real-Time Decisions To Prevent Fraud Activities

If a business is able to predict which transactions can lead to fraudster attacks, then the business can considerably lower costs and make critical decisions. While sending sensitive data to a third-party, it is important that the data is not misused for fraudulent activities. This can be done as follows:

• Using Machine Learning Models

Consider a score produced from a number of algorithms that is a combination of all possible features. This set of algorithms can be termed as a machine learning model. This machine learning model constantly queries these algorithms in order to produce an accurate score that can be used to predict frauds.

Machine learning models can be compared to data analysts who run numerous queries on large volumes of data and try finding out the best from the derived outcomes. Machine Learning makes the whole process fast and accurate.

• Fraud Scores For Fraud Detection

There always exists large amounts of data. Machines are trained using these data sets that are pre-labeled as frauds. These labels are based on earlier records of confirmed fraudulent activities.

The machines are then trained using this labeled set of data. These data sets are now called as training sets. By a named label, the machine is taught to determine if a new transaction or a particular customer is likely to be a fraudster based on a score of 0 to 100, being the probability.

This score enhances the ability of a business to ensure a considerable reduction in frauds by providing accurate predictions.

Related Reading: Check on to this Infographic to learn more about Machine Learning.

Can Machine Learning Actually Predict And Prevent Fraudsters?

Designing as well as being able to apply algorithms that are on the basis of data sets from the past, enables to analyze frequent patterns in these data sets. These patterns in data via the algorithm are taught to machines and these machines considerably reduce human effort.

These algorithms help businesses boost predictive analysis. Predictive analysis is important for data reduction by using statistical modeling techniques that help in predicting future business outcomes on the basis of past data patterns. In fact, among many businesses, 75 percent of them find growth to be their main source of value, whereas 60 percent of some others believe that it is nothing else but predictive analytics that is the key to deriving value!

Machine learning algorithms are not only used in predictive analytics, but also in image recognition, detecting spam, and so on. Machine Learning can be trained by a 3 phase system.

1. Train

2. Test

3. Predict

So to be able to predict an occurrence of fraud in large volumes of data sets and transactions, cognitive technologies of computing are applied to raw and unprocessed data.

Machine Learning thus facilitates, prediction and prevention of fraudsters for the following key factors:

• Scalability: Larger the data sets, increased is the effectiveness of machine learning algorithms. Initially, the machine learns which transaction/data sets are fraudulent and which ones are safe, the machines are well able to predict such cases in future transactions.
• Readiness: Manual tasks are time-consuming. These are not preferred by clients. Hence, machine learning strategies are used to acquire faster results. Machine learning algorithms process a large number of data sets in real-time to customers. Machine Learning frequently and periodically analyzes and processes new data sets. Advanced models like neural networks have provisions for autonomous updations in real-time.
• Productivity: The need to perform redundant tasks reduces productivity. The continuous repetitive task of data analysis is performed by Machine Learning algorithms and prompts for human intervention only when required.

Related Reading: Check out how machine learning is revolutionizing software development.

Machine Learning Methods – Using White Boxes And Ongoing Monitoring To Detect Fraudsters

What does a machine learning system do? The methods adopted and the various approaches used for this are termed Whiteboxes, as there is no definite method or model to analyze the score obtained. Similarly, regular and ongoing monitoring is critical for a machine learning system to identify the trends and data statistics on a regular basis.

How Fraudsters Are Detected And Prevented By Using Machine Learning

Data sets are initially collected and partitioned. The machine learning model is taught the sets in order to predict data fraud. The following are the steps in which Machine Learning implements and performs fraud detection:

• Data Partitioning: The data is segmented into working in three different phases such as training the machine, testing for data sets and finally, cross-checking of the prediction results.
• Obtaining Results of Historical Data: To obtain such data sets, training sets have to be first provided to the machine that includes input values associated with its corresponding output values. This helps in predicting and detecting frauds.
• Predicting Anomalies, If Any: Based on the input and output data, predictions are determined by analyzing the anomalies or fraud cases in the data sets. For this, building models are used. This can be done by many techniques such as using Decision Trees, Logistic Regression, Neural Networks, and Random Forests, etc.
• Out of the techniques, Neural Networks are quick in processing results by analyzing data sets and helps in making decisions in real-time. It does so by observing regular patterns of frauds in earlier cases of data sets given to it for learning.

In a nutshell, Machine Learning is proving to be the right technology in detecting and preventing fraudsters from malicious activities. If banks start using machine learning systems, it could analyze unstructured data and prevent customer’s accounts from fraudulent activities. To know more about how you can empower machine learning and other technology trends to secure data, get in touch with our IT experts today!

There were times when common banking activities like withdrawal of money or deposit of money meant time-consuming visits to the bank and waiting in line for hours. As inconvenient and troublesome as that was, it also meant less risk of being cheated and less chances of theft of personal information. Today, we have banking at our fingertips, with mobile banking applications. What we used to do in probably three to four hours earlier, can be done now with a few simple clicks or taps in a matter of seconds and that too from anywhere in the world if you have a smart device and the internet. But how secure are these mobile banking apps and online transactions? Could someone be stealing all the information you send out through such apps?

The truth is that there are various risks of being victims to such cyber attacks, but thankfully mobile banking apps these days are made with several layers of protection and it would take considerable effort for a thief to break in through all of that. Nevertheless, there are some things that you should do too, to make sure that your information is safe and protected while using mobile banking apps. Here’s what you should do for a safe banking experience online:

1. Avoid following links and thus phishing – Phishing refers to the practice of acquiring personal and important information such as passwords, usernames and the like by pretending to be a trustworthy person or entity. It basically involves tricking someone into providing sensitive information and then using that information for malicious practices. It is in a way similar to actual ‘fishing’ as both of them involve bait to lure the victim into the trap. The bait in phishing could be a simple text message with an internet link or an email, or even a website, which could even look like your bank’s website (spoofing). You should never follow links on such emails and messages. According to the Federal Trade Commission, these are the most common ways that scammers use to steal private information from you. Legitimate companies and banks never ask for credit or debit card numbers, passwords or social security numbers and the like through emails and texts, so if you see a message somewhat like this:
   “We suspect an unauthorized transaction on your account. To ensure that your account is not compromised, please click the link below and confirm your identity.”
   DO NOT click on the link.
2. Download only the official banking app from a reputed site or store – Most leading banks offer their official applications on most smartphone app stores. For a fact, such apps are a lot more safe and secure than other apps and are definitely better than sending SMSs and emails. Banks actually go a long way to ensure that any information entered and sent across through their apps is encrypted. Hence make sure you download only the official app and only from leading well-known app marketplaces, like that of Google’s and Apple’s. Your bank will probably have information about their official app on their website, which you can use to verify the credibility of the app you are about to download.
3. Avoid banking through public networks – Public WiFi networks are often not that secure. Even simple banking activities like checking your balance can make your private information vulnerable over the network. Most places that offer such WiFi or hotspot networks often advise their users not to share private information through it. It is always better to switch to your phone’s network in case you have to use your banking app while in a public network, as fraudsters and thieves could also be using the same network and may be waiting to steal your information. Some phones allow automatic switching to WiFi once they are in range of an open one. Hence, make sure you are not on a public network before using online banking apps or sharing any private, sensitive data.
4. Avoid having personal data on your phone – For the same reason that your phone is the most convenient tool that you can easily carry around everywhere, it also proves to be sort of a risk sometimes. Your phone can have all kinds of information, like your calendar appointments, your passwords and your contacts. Such information can be read by other apps and other devices if not properly monitored and can be used against you. It also means you should be careful about where you leave your phone. Try avoiding maintaining private information on your phone as much as possible. If at all you have to, make sure you have several layers of protection for your phone. This will at least give you some time to lock your phone in case it gets stolen or lost.
5. Make sure you have an updated anti-virus system – Keeping your system up to date with the latest software along with the latest security patches can actually help a great deal in keeping away malicious software and people. It is also good to download an appropriate, trusted antivirus program for your phone as well. That adds as an extra layer of protection to the phone as well.

Keeping all these points in mind, you can very well use mobile banking apps safely. All you need to do is be a little careful and alert. In spite of all this, if you happen to lose your phone, you have the Federal Laws to the rescue. According to CNN, you can recover your losses with a limit of $50 if you report the loss within two days of its discovery. Several banks apparently already have policies in place, that waive the liability completely. Even then, prevention is better than cure, so it’s always better to avoid theft and follow these simple steps while using mobile banking apps.