Tag: Artificial Intelligence

How AI is bringing change to the software testing practice

Artificial Intelligence is penetrating into multiple functions performed by the software industry. In software testing, the technology holds the potential to be a game-changer. Imagine the capability of your software to test and diagnose itself and make self-corrections! This will lead to huge savings on your resources. With this in mind, let’s try and understand exactly how AI will impact the traditional way of software testing. 

Before we proceed, let’s get one thing clear – Do we really need AI in software testing?

Do We Really Need AI in Software Testing? 

Software testing came into existence as a result of the evolution of development methodologies. It fed the need for robust, error-free software products. Testing was a laborious task for sure. However, automating software testing required traceability and versioning, both of which were critical and needed careful consideration. Something was needed to resolve this.

As businesses move towards digital transformation and the software market continues to grow, businesses expect a real-time risk assessment across all stages of the software delivery cycle. AI in software testing is the right response to these challenges. AI can develop error-free applications while enabling greater automation in software testing. This helps meet the expanded, critical demands for testing. It improves the quality of engineering and reduces testing time allowing the tester to focus on more important things. The verdict is clear then – We Really Need AI for Software Testing!

Five Impressive Ways AI Impacts Software Testing

1. Improves object application categorization

AI is widely used in object application categorization. When tools and testers are created, unique pre-train controls can be created. Once the hierarchy of the controls is categorized, testers can create a technical map to obtain labels for the different controls. 

In the near future, AI will become capable of observing users perform exploratory testing on the testing site. And once user behavior is assessed, it can assign, monitor, and categorize the risk preference.

2. Automation of test case writing 

Gone are the days of web crawlers. As automation is picking momentum, AI tools have become capable of learning business usage scenarios of test applications. 

Related Reading: Unconventional Ways Artificial Intelligence Drives Business Value

They can automatically collect insightful data such as HTML pages, screenshots and page loading time and eventually train ML models for expected patterns of the app. And as soon as they are executed, any variations are marked as potential issues. This makes it easier for the tester to find and validate differences and fix issues. 

3. Enhanced accuracy

To date, source analysis requires human resources to accomplish the task. Unfortunately, because of the enormity of the data, even the best experts could overlook, or miss out on observing certain critical defects. Human error and the tendency to lose focus further impairs the experts involved in software testing. It can be disastrous if bugs caused by these errors are caught by consumers before project stakeholders. Product positioning and brand reputation can be jeopardized. 

Thankfully, AI can teach systems to learn source analysis and, in the future, apply this acquired knowledge. This ensures that testers have greatly enhanced accuracy. It eliminates the probability of human error and also shortens the time to run a test and increases the possibility of finding defects or bugs. 

4. Automation without the user interface

AI-based techniques can be applied for non-functional tests such as performance, security and unit integration. It can also be applied on various application logs which assists in developing auto-scaling capabilities such as bug prediction.

AI algorithms can enhance UI testing, predict the next test, determine the outcomes for subjective and complex tests and much more. In other words, AI could increase the overall test coverage while it increases the depth and scope of the test itself.

5. Reduces cost and decreases time to market

The need for manually repeating a test is time-consuming and extremely expensive. But with AI, such tests can be automated to repeat several times over. Each time the software test is repeated automatically, the source code gets modified to correct any bugs. This eliminates the additional cost of repeating the test and increases the speed of the test from days to hours, which in turn saves more money.

Related Reading: Quality Assurance in Software Testing – Past, Present & Future

Allow AI to Revolutionize your Business

AI has proven to have a significant impact on software testing with its benefits ranging from optimization to extraordinary savings. It enables testers to move beyond the traditional route and dive toward precision-based testing processes. This can prove invaluable to your business. To find out how you can make this happen for your business, contact us. 

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 AI and Voice Search Will Impact Your Business in 2020

“It is common now for people to say ‘I love you’ to their smart speakers,” says Professor Trevor Cox, Acoustic engineer, Salford University. 

The Professor wasn’t exactly talking about the love affair between robots and humans, but his statement definitely draws attention to the growing importance of voice search technology in our lives. AI-driven voice computing technology has drastically changed the way we interact with our smart devices and it is bound to have a further impact as we move into 2020. 

In this blog, we will consider six key predictions for AI-Driven voice computing in 2020.

How Essential Is AI-Driven Voice Search For Businesses?

Voice search is becoming increasingly popular and is evolving day after day. It can support basic tasks at home, organize and manage work, and the clincher – it makes shopping so much easier. No doubt about it, AI-driven voice search and conversational AI are capturing the center stage. 

Related Reading: Why you can and should give your app the ability to listen and speak

Voice-based shopping is expected to hit USD 40 billion in 2022. In other words, more and more consumers will be expecting to interact with brands on their own terms and would like to have fully personalized experiences. As the number of consumers opting for voice-based searches keeps increasing, businesses have no option than to go all-in with AI-driven voice search. With that in mind, let’s see where this is going to be leading businesses in 2020. 

Six key predictions for AI-driven voice search and conversational AI in 2020

1. Voicing a human experience in conversational AI

Chatbots are excellent, but the only downside is that most of them lack human focus. They only provide information, which is great in itself, but not enough to provide the top-notch personalized experience that consumers are looking for.  This calls for a paradigm shift in conversational design where the tone, emotion, and personality of humans are incorporated into bot technologies. 

Statista reports that by 2020, 50% of all internet searches will be generated through voice search. Hence, developers are already working on a language that would be crisp, one that is typically used in the film industry. Such language could also be widely used on various channels such as websites and messaging platforms. 

Related Reading: Capitalizing on AI Chatbots Will Redefine Your Business: Here’s How

2. Personalization

A noteworthy accomplishment in voice recognition software enhancing personalization is the recent developments in Alexa’s voice profiling capabilities. Personalization capabilities already in place for consumers are now being made available to skill developers as part of the Alexa Skills Kit. This will allow developers to improve customers’ overall experience by using their created voice profiles.

Such personalization can be based on gender, language, age and other aspects of the user. Voice assistants are building the capacity to cater even to the emotional state of users. Some developers are aiming to create virtual entities that could act as companions or councilors. 

3. Security will be addressed 

Hyper personalization will require that businesses acquire large amounts of data related to each individual customer. According to a Richrelevance study, 80% of consumers demand AI transparency. They have valid reasons to be concerned about their security. This brings the onus on developers to make voice computing more secure, especially for voice payments.

4. Natural conversations

Both Google and Amazon assistants had a wake word to initiate a new command. But recently it was revealed that both companies are considering reducing the frequency of the wake word such as “Alexa.” This would eliminate the need to say the wake word again and again. It would ensure that their consumers enjoy more natural, smooth and streamlined conversations.

5. Compatibility and integration

There are several tasks a consumer can accomplish while using voice assistants such as Amazon’s Alexa or Google’s Assistant. They can control lights, appliances, smart home devices, make calls, play games, get cooking tips, and more. What the consumer expects is the integration of their devices with the voice assistant. 2020 will see a greatly increased development of voice-enabled devices.

6. Voice push notifications

Push notification is the delivery of information to a computing device. These notifications can be read by the user even when the phone is locked. It is a unique way to increase user engagement.  Now developers of Amazon’s Alexa and Google Assistant have integrated voice push notifications which allow its users to listen to their notifications if they prefer hearing over reading them.

What Does It Mean for Your Business In 2020?

AI-driven voice computing and conversational AI is going to change all aspects of where, when and how you engage and communicate with your consumers. By 2020, IDC  estimates a double-digit growth in the smart home market. Wherever they are and whatever channel they are using, you will be required to hold seamless conversations with your customers across various channels. 

“Early bird catches the worm.” Be the first in your industry to adopt and gain the benefits of voice search and conversational AI.  Call us and find out how we can make this happen for you.

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. 

Facial Recognition Technology – What’s In Store For The Future

When Facebook started automatically tagging faces in uploaded images, nobody realized that this facial recognition technology would hike up to tracking people down while walking on the streets. In the past several years, this disruptive technology has gained immense popularity, that it is now used everywhere, from airports to shopping centers, to law enforcement. With its growing predominance in national safety and security, the face recognition market is estimated to reach USD 11.30 Billion by 2026. 

“Facial recognition has been around for a long time—like the 1960s. Perhaps the father of facial recognition, Woodrow Wilson Bledsoe, an American mathematician and computer scientist who classified photos of faces all by hand, (RAND tablet), even he might have been alarmed at how facial recognition technology is supercharged today by advances in computing power, 5G speeds and AI paired with machine learning.”   

– Tamara McCleary, CEO of Thulium, and a unique advisor to leading global technology companies such as SAP, Dell, Oracle, IBM.

Moreover, the advancements in artificial intelligence and machine learning are bringing about an active expansion to this technology. It won’t be long when the automation of facial recognition technology will fundamentally change the way we do many things. However, many minds still doubt on the path this revolution is leading to. 

Let’s dig deeper into the advancements of the facial recognition technology, what it holds for the future and whether it’s completely safe to rely on such a disruptive technology that fiddles with personal identities.

Facial Recognition Technology In-Depth

So what is facial recognition technology and how exactly does it work?

Facial recognition is a biometric technology that utilizes unique facial features to recognize individuals. Today’s plethora of innumerable photos and videos make the dataset for this technology to work. Through artificial intelligence and machine learning capabilities, software mathematically maps distinguishable facial features, to compare patterns in newly available images with visual data stored in the database. Such a recognition process allows the simple unlocking of phones to security checks at airports. 

In a way, artificial intelligence plays a vital role in the complete identity recognition process. A branch of artificial intelligence known as computer vision works through measuring nodal points on a face to make a face-print. This faceprint is a unique code that is applicable only to a particular person. This enables identification.

“I believe AI in Facial Recognition could add great value to society but we have to be careful to use clean data and we have to educate the public for the need for good, clean, accurate data to be sure we do not accidentally disenfranchise certain groups even more in the future. We must assure the data does not include unconscious bias or even deliberate bias programmed into the code. It is also important to note that we have a major lack of data for many disenfranchised groups including the community of persons with disabilities.”

– Debra Ruh, CEO, Ruh Global IMPACT, Global Disability, and Aging Inclusion Strategist.

Once this faceprint is made, the technology runs through an identity database to match this face with a name and other required details. Thus, the probability of error is near to rare; maybe an eight out of 1000 scans could mistakenly identify the person. This is what makes this technology an excellent prospect for performing crucial functions.

Read more: How Fingent helped develop a unique mixed reality application for a leading university to identify people using facial recognition

Innovative Uses Of Facial Recognition Technology

As facial recognition technology evolves with time, few industries and countries apply the technology in innovative ways.

China is rising to be the leader in facial recognition technology. Although part of the technology remains a perspective, its innovative use is what amazes the audience. A few other countries following the trend are Japan and the United Arab Emirates. The US doesn’t stand back either. Look at these impressive ways of face recognition technology implementation.

• Face recognition is on its go, replacing cash and credit cards. At fast-food units like KFC, customers can just smile into a self-serve screen to automate the identification and withdrawal of cash from banks. Some banks are also allowing customers to use face recognition instead of bank cards.
• The automobile brand Subaru has integrated facial recognition cameras to its Forester brand of SUVs. This is intended to detect when a driver is tired or about to sleep to take necessary actions to prevent accidents. This indeed is a tremendous innovation towards road safety.
• The 2020 Tokyo Olympics, is reported to make use of facial recognition to boost their security systems. Instead of relying on ID cards that have a high probability of being fake, the authorization is now implementing the FR technology to allow media, competitors or other such people to enter the premises.
• Dubai Airport also makes use of the FR technology to strengthen their security. A virtual aquarium fitted with 80 facial recognition cameras examines every passerby to easily recognize criminals or offenders. Also, police cars are on their go-to implement FR cameras to identify criminals and wanted vehicles quickly.
• Facial recognition technology is no doubt making a great impact on national security systems, promising a safe and crime-free future. The US government is also making use of biometric exits and AI cameras to track people crossing their international boundaries without proper documents. 

The Growing Concern

Though face recognition technology offers innovative and impressive use cases in security and surveillance, there are numerous challenges that it faces. Privacy being a major concern, not everybody is happy with the storage of sensitive and personal data. A potential downside of this technology is the data and privacy breaches. The databases containing facial scans and identities are being used by multiple parties such as banks, police forces, and other defense firms and are hence prone to misuse. 

Considering the face recognition tech as a threat to their citizens’ privacy, many cities including San Francisco, Massachusetts, Cambridge, and others are planning to put a complete ban on real-time face recognition surveillance. 

“Concerns around AI’s practical applications like facial recognition have begun crystallizing over the last few years and will continue unabated. Current AI-based face recognition systems possess a grave threat to individual privacy, which if unregulated may end up jeopardizing sensitive user data to the wrong hands in times to come.” 

– Varghese Samuel, CEO & MD Fingent.

Moreover, how much can this technology eliminate crime is still being discussed. The accuracy of the system in detecting people who cover their faces from cameras or disguise themselves is yet a topic of dispute. However, to everyone’s relief, the technology is showing constant improvement in this matter. According to the U.S. National Institute of Standards and Technology (NIST), facial recognition systems got 20 times better at finding a match in a database over a period that covered 2014 to 2018.

“Artificial intelligence has made great strides, but still has a long way to go. It is powerful to use on a daily basis, when the stakes are low (for example, in tagging photos or recommending advertisements), but not yet trustworthy enough to stand fully on its own in high-stakes applications, such as driverless cars, medical diagnosis, and face recognition, where errors can deeply affect people’s lives. 

– Gary Marcus, Founder and CEO, Robust.AI Professor Emeritus, New York University, Author of book: REBOOTING AI

The Untold Future

It is pretty much tough to predict where the facial recognition technology would be in the coming years, but the increase in AI advancements is sure to widespread this technology around the globe. Major industries have already capacitated the FR capabilities to replace the traditional process of paying bills, opening bank accounts, checking controls at airports, and such. A few of these industries include healthcare, retail, marketing, and social media platforms. 

In a nutshell, face recognition technology is expected to predominate the globe in the near future. The increasing usage of mobile devices and demand for robust fraud detection and prevention is predicted to majorly drive the implementation of this technology. As per the predictions made by Markets and Markets, a prominent research firm, the global facial recognition market size is expected to grow from USD 3.2 billion in 2019 to USD 7.0 billion by 2024, at a Compound Annual Growth Rate (CAGR) of 16.6% during 2019–2024.

“The more people grow accustomed to using facial recognition products and services that enhance efficiency and that can, at the moment, seem altogether too fun or mundane to be harmful — whether it’s tagging photos, unlocking a phone, or projecting how your face might look in the future — the more facial recognition technology becomes normalized.”

– Jarno M. Koponen, Head of AI & Personalization at Yle News Lab. His work has been covered by The New York Times, New Scientist, Oxford Reuters Institute, Mashable, TechCrunch.

Face recognition technology is revolutionizing the world more than you think. It’s time to figure out how this technology could bring added value to your firm. Contact our experts today!

How to Attain More Business Value with AI Implementation

The potential of Artificial Intelligence (AI) is being harnessed by businesses to implement automation across several industries and to augment their efforts to provide stellar customer experiences. The incomparable precision, speed, and accuracy of AI-enabled solutions are driving an AI revolution and this blog will discuss five specific ways you can attain business value with AI adoption.

AI: A new approach to customer experience

IBM, one of the leading universal AI champions reveals that 74% of customer experience executives feel that AI will change how customers view their brand. AI provides customers with an experience that appears very similar to human-like interaction. AI-powered apps are highly efficient in enhancing customer interactions. 

Automation of “the customer experience journey” through AI enables your business to personalize your marketing campaigns, which will boost the email opening rate. Among other things, AI provides improved customer insights and better customer satisfaction which results in increased conversion and strengthens brand loyalty. 

Related Reading: How AI is Redefining the Future of Customer Service 

Steps to Gain Business Value with AI Adoption

A study from Market Research Engine estimates that the Artificial Intelligence Market is expected to exceed more than US$ 191 Billion by 2024. The vast growth of information in huge quantities, growth in the adoption of cloud-based applications and services, and an increase in the demand for intelligent virtual/ personal assistants are the major driving factors behind the booming AI market. 

Unfortunately, many are still unsure of how to adopt AI, and how it can be used to yield maximum results. We present you these 5 steps to adopt AI which will help you gain the maximum business advantage. 

1. Map out a clear customer-centric strategy

The best way to do this is to analyze recent customer journeys with your brands such as discovery, presales, sales, and customer service. Such analysis will help you understand the experience your customers are having with your brand. Research director at Gartner, Olive Huang said, “Your business results depend on your brand’s ability to retain and add customers.” Hence it’s important to deliver a highly personalized experience to every one of your customers. Once that strategy is crafted, it can be delivered through AI.

Related Reading: 6 Ways Artificial Intelligence Is Driving Decision Making

2. Diagnose the problem

Just as the prudent diagnosis of a disease is crucial to helping a patient improve their health, finding out your specific business problems will help you use AI to improve business value. Organizations need to be clear about what AI can help them accomplish. Once the problem is identified, aligning AI to business priorities becomes easier. The beauty of AI algorithms is that once algorithms are enabled to solve one aspect of the business successfully, it can easily be adapted for use in other aspects of the business too.

3. Establish a governance program for customer experience

Before you begin your journey with customer experience, make sure to establish a governance program. The benefit of having a central team working on the AI project is that you can integrate program oversight with a complete comprehension of AI-related initiatives. This is true even when your customer experience teams are just in the learning process. Build a program to supervise development activities and business implications and set brand guidelines for AI technologies such as NLP. Also, establish standards to gauge the impact of customer experience initiatives and its correlation with ROI.

4. Bring the best onboard

Creating a custom AI solution is recommended over buying it readymade. It is important however that you bring in the best talent. Bringing in a team of dynamic people who can create exciting ways to engage with the customers sends a powerful message to your competitors. It will also put your organization on the radar as a tech-savvy innovator. 

Read our latest white paper: How Could Your Business Use AI to Achieve Higher Profits & Growth

5. Prepare to store

A business that fails to look into the future fails to grow. Gathering relevant data allows organizations to derive greater benefits far into the future. Meaningful data help AI systems in achieving your organization’s objectives. Insufficient data could compromise the accuracy of AI applications, so get your team to build relevant data including cases and codes.

Drive Business Value With AI 

Organizations are adopting AI faster than anticipated. To gain long-term and sustainable business value from Artificial Intelligence, you need to develop a robust implementation approach that takes into its fold, even the minute aspects of your enterprise. Contact us to adopt the power of AI into your business.  

How AI and Machine Learning are Driving Cyber Security in FinTech?

Being a subset of the financial services domain, FinTech is targeted by hostile cyber villains. Industries thus require secure mechanisms to keep their data safe and secure. Preventing data losses are critical for Fintechs. 

The World Economic Forum states that cyber-security is the Number One risk associated with the financial services industry.   

Cyber Security Risks Associated With FinTech

Cybersecurity has remained a pressing concern for FinTech. Ever since the global financial crisis of 2008 that challenged the traditional financial institutions significantly, technology-driven start-ups have started evolving increasingly to cater to finance, risk management, digital investments, data security, and so on. Presently, we are in the FinTech 4.0 era. 

The major cybersecurity risk that enterprises implementing FinTech face are from integration issues such as data privacy, legacy, compatibility, etc. Hackers target FinTech as they handle large volumes of customer data that include personal, financial, and other critical information.

FinTech offers a multitude of easily accessible services via its APIs. For instance, API banking. Here, the APIs are developed for the banks to access the FinTech platforms. It becomes open, API banking when open APIs enable third-party developers to build banking applications and services. 

Let us walk through the major cybersecurity challenges triggered by FinTech.

• Data Integrity Challenge

Mobile applications deployed for FinTech services play a predominant role in cybersecurity assurance. FinTech services require strong encryption algorithms to avoid integrity issues that can arise while transferring financial data. 

• Cloud Environment Security Challenge

Cloud computing services such as Payment Gateways, Digital Wallets including other secure online payment solutions are key enablers of the FinTech ecosystem. Though it is simple to make payments via cloud computing, it is equally crucial to maintain the security of data as far as banks are concerned. Appropriate cloud security measures are thus critical while dealing with sensitive information.

• Third-Party Security Challenge

Third-party security challenges include data leakage, service challenges, litigation damages, and so on. Banks should be aware of FinTech service relationships while associating with third-parties. 

• Digital Identity Challenges

Major FinTech applications are web apps that have mobile devices working at the front-end. Banks and other financial institutions need to learn about the security architecture of the electronic banking services offered by these applications before implementing the FinTech application.

• Money Laundering Challenges

The use of cryptocurrency for financial transactions makes FinTech-drive banks prone to money laundering activities. Thus, the FinTech ecosystem needs to be formally regulated based on global standards.

• Blockchain Challenges

Private keys can be stolen in case of weak security in blockchain architecture. Cryptographic algorithms need to be strong and transactions need to be confidential.  

The increase in the number of FinTech implementation of interfaces will cause a rise in the number of cybersecurity challenges as well. 

Can Machine Learning Predict And Prevent Fraudsters?

How Artificial Intelligence And Machine Learning Enables Cyber Security For FinTech?

Artificial Intelligence is both reactive as well as proactive or preventative. AI reinvents FinTechs by bringing in behavioral biometrics solutions. These solutions are used to monitor customer and device interactions that take place during transactions that enhance security and authentication. BB or behavioral Biometrics with AI provides problem-solving capabilities for FinTechs. FinTechs utilize Artificial Intelligence is an expert system that enhances decision-making abilities using deductive reasoning. Big Data analytics is used here to focus on quality data. 

The underlying technology in using Artificial Intelligence involves reasoning, learning, perception, problem solving, and linguistic intelligence to provide critical insights. It helps in understanding business in real-time operations. 

In this digital era of increasing cybersecurity attacks and malpractices, AI can be used effectively to prevent risks and attacks. The following are major ways of how AI and ML protect FinTechs:

1. Fraud Detection

AI and machine learning algorithms are used to detect frauds in FinTechs by being able to identify transactions in real-time accurately. The traditional strategy of fraud detection involved analyzing large volumes of data against sets of defined rules using computers. This process was time-consuming and complex. Unlike this traditional method, more intelligent data analytics tools for fraud detection such as KDD (Knowledge Discovery In Databases), Pattern Recognition, Neural Networks, Machine Learning, Statistics, and Data Mining have evolved. 

2. Controlling Access

Access control to critical data is crucial when it comes to security. Machine learning is used to derive critical insights from previous behavioral patterns such as geolocation, log-in time, etc to control access to endpoints. The risk scores are fine-tuned by combining supervised and unsupervised machine learning methods to reduce fraud and thwart breach attempts as well. 

3. Smart Contracts

Smart contracts are coded in a programming language and stored on the blockchain. With blockchain, new contracts can be added to existing ones without having to change the individual contracts, in case of agreement expansion. Artificial Intelligence has become an integral part of FinTech as more traditional banks are teaming up with FinTechs to leverage the benefits of both worlds. For instance, when customers face issues with a poor credit history while applying for loans. 

Artificial Intelligence is yet to be transforming the face of FinTechs in a multitude of ways. Drop-in a call right away and our strategists will guide you on how to leverage the benefits of AI and ML to secure operations and prevent breach attacks.

How Chat Bots Can Enhance Student Onboarding

Conversational interfaces have gone mainstream. The technology behind keeps crossing new milestones, the result of which chatbots have transformed from simple Q&A systems to intelligent personal assistants. As a result, bots found widespread application in diverse areas, most recently in education. 

Although education stayed backward in terms of technology adoption, lately it took on a renewed quest to incorporate it. Educators are on the lookout for innovative ed-tech systems for efficient tutoring and students increasingly prefer personalized learning environments. 

Deploying chatbots at numerous front-ends like college/university websites, internal student communication portals or even popular instant messaging platforms can help with that. Here’s how? 

Chatbots bring in a personalized and engaging learning experience optimized to the learning pace of each learner, which actively drives student-centered learning at the forefront. Configuring a bot to answer student inquiries related to curriculum, courses, admissions, etc. as well as deliver learning resources on request makes way for a personal always available assistant that every student can engage with. 

That’s exactly what we did, though in a different way. 

Recently Fingent was approached by a client, a leading public research university based in Australia to develop an intelligent chatbot for assisting prospective and freshly enrolled candidates with onboarding and orientation, course-related information, credit scores, etc. The client wanted to streamline its entire student orientation process using a chatbot and make all related information better accessible and context-based as well as systematically tackle the ‘summer melt’ rates. 

Here, we lay down a high-level abstract of this chatbot development experience powered by IBM Watson Assistant and backed by .NET Core. It briefs various facets and challenges faced during the design and development of the system.

The Plot (Objective)

“Build a chatbot to assist candidates during the orientation process of Monash University. The chatbot should be capable of handling different context-based scenarios such as listing available courses, providing credit score information, course structure, projects associated with each course and many more.“

Since it is a Proof of Concept (POC) project, and Monash University offers a wide range of courses based on various areas of education, the team decided to choose one particular area and focus on only two of the selected courses (Bachelor of Accounting & Bachelor of Actuarial Science). This is to repress the scope in control, considering the timespan and resource availability.

Foundation

Keeping in mind the idea of building a highly sophisticated chatbot, an ideal and matured chatbot assistant technology had to be finalized, which provides both comprehensive user intent identification and processing as well as a satisfactory response according to the user query. The system should also provide an extensive and less technicality included training interface. The hunt for such a tech ended up in IBM Watson Assistant.

Terminologies

The world of chatbots has some common terms which are essential key knowledge required while developing a chatbot. We can call them as the pillars of a chatbot.

• #Intent – Intent is nothing but the user’s intention in a query – basically covers all types of questions and their varieties, the user probably may ask. This can be queries within the scope or related to the scope.

Examples:

“What are all the courses available?”
– Intent associated: #KnowCourseInfo

“How much credit I require in the first semester?”
– Intent associated: #KnowCreditInfo

Remarks – There will be some stock #intent collection depending upon the chatbot engine, which is designed to handle the general greetings and conversation-oriented chunks. We can import or enable the intents as we want to make our chatbot more conversational and human-friendly.

• @Entity – An entity is a subject addressed in the user query. There are mainly two categories of entities. They are Scope-based entities and System entities. Scope-based entities are entities that belong to the scope we address whereas System entities are “primitive system-aware” entities.

Examples:

“What are all the courses available?”
– Entities associated: @Course

“How much credit I require in the 1st semester?”
– Entities associated: @Credit, @Semester, @system_number:1st

Remarks – On diving deeper, we may need the support of multiple types of scope-based entities and a system-aware way of specifying the relationship between the entities (which lacks in IBM Watson Assistant). This is to specify the entity characteristics as more descriptive as well as with the notion of “the system knows” the given attributes and relationships of an entity.

• Dialog – A dialog is a declarative way of specifying the possible questions the user may ask, and how should the bot respond to the corresponding questions. Generally, this will be a tree-based structure, rooted in the key user intentions and scope covered features. We will be handling the different scenarios of a single #intent as well as the edge cases.
• $Context Variable – A context variable is to store information, collecting from a dialog context or it can be any information related to the dialog context. It helps us to keep the dialogue context and facilitates conversational flow.
• Skill/Workspace (IBM Watson based) – A skill is a package that consists of the above-mentioned factors, in which all are aligned into a single chatbot capability, in our case, it was Onboarding skill.

Implementation

The entire development process streamlined into two major sections. The first one is aligned to the chatbot engine intelligence building and improvement activities while the other one is for the middleware and UI development.

1. Intelligence Build-up on top of IBM Watson Assistant

• Analyzed the requirements and fixed the boundaries of the scope. It includes what all are the functional areas to be covered by the proposed chatbots.
• Prepared the possible user queries and categorized them as #intents.
• Identified the underlying @entities in each question and classified them to form the actual set of primitive entities.
• Designed the dialog structure based on the prepared user query sets. See the resources: Intent structure and Dialogue flow. 

Fig 1. Intent Structure

• Continuously refined the dialogue structure based on detecting each edge cases and to incorporate new scenarios.
• Used some conventions on responses to extend the chatbot response capabilities, according to the requirements. This is to handle specific use cases such as clickable action list image response, map response, and show a list of items.
• Implemented WebHooks (IBM Watson based) to talk to external APIs to fetch the values for a dialogue node as well as validating user input (Not a comprehensive solution).

2. Middleware and UI Development

• Built a middleware backed by .NET Core with an intention to plug any chatbot service to the UI module. In fact, it is designed as a standard-framework to separate the chatbot logic from the application logic. This enables hassle-free maintenance of the app logic, code reusability, and extensibility.
• Built the UI using Angular to provide a sophisticated face for our chatbots.

Fig 2. Dayton Interface

Also, we built a diagnostics module, as part of the UI, which provides the service configuration information and session-based transcripts of conversations held with the chatbot.

Fig 3. The architecture of the Chatbot Middleware Application, Source Code

Challenges

During the development, we came across some development challenges with IBM Watson, which are listed below.

1. Unable to map relationships between entities. Due to this limitation, we were unable to link and pull the related values of the entities.
2. Conflicts between various entity values (Solved partially via entity split-up method)
3. API Limitations to manage chatbots dialog schema
4. IBM Watson doesn’t provide active learning, the self-learning capability to learn from user conversation sessions.
5. It also doesn’t provide an efficient way to talk to external APIs. Only one external API can be called, which leads to a bottle-neck on executing the webhook actions.
6. No built-in user input validation. This has to be done via WebHooks.

Capitalizing on AI Chatbots Will Redefine Your Business: Here’s How

Final Words

The application is now in a showcase/UAT (User Acceptance Testing) mode, also the refinement process being in progress. It has miles to go to reach the capability to converse with the user as a comprehensive onboarding assistant.

To know how chatbots can enhance your business growth, get in touch with our experts today!

Can Empowering AI and IoT Bring In Competitive Advantage To Industries?

It takes more than forward-thinking employees to gather customer purchasing trends and improve the customer experience. International companies depend on Artificial Intelligence (AI) and the Internet of Things (IoT) to drive data and forecast the next big wave of trends.

Studies predict Asia and North America to lead in the innovation of AI and IoT. Also, embedded AI in support of IoT smart objects will reach $4.6B globally by 2024.

Major vendors of IoT platforms such as IBM, Amazon, and Microsoft have started offering integrated AI capabilities like ML-based analytics. Scalable digital platforms are designed daily to understand the way customers think while using predictive maintenance in real estate, eCommerce, healthcare, and other industries.

It’s time for us to share the leading examples of how businesses use AI and IoT, and how these technologies benefit them.

AI and IoT: Leading Use Cases

Smart Cities: Making Life Easier

What happens when AI and IoT run a city? It turns into a smart city. Smart city technology can solve an energy crisis, help manage traffic, or improve the healthcare experience. 

One example of a smart city is the use of Advanced Transportation Controller technology linked to a 5G network in Los Angeles. There are road-surface sensors throughout the city, and cameras that monitor traffic, sending information to traffic management systems. Municipal employees can now analyze the data of traffic congestion and issues with traffic lights in high traffic areas. Overall, this improves the quality of living in Los Angeles and helps a business run smoothly without delays.

Convenience in Property Management

One of Fingent’s clients WRI Property Management, a US-based single-family rental provider with 10,000+ leased properties and 20,000+ managed houses experienced many challenges. Here are a few of the issues WRI Property Management faced:

• Tenant eviction
• Rent collection/accounting
• Scheduling property inspections
• Leasing properties
• Screening tenants

What happened next? Fingent introduced an advanced software platform, Honey Badger. The AI and IoT technology-supported WRI managers to conveniently communicate with multiple parties, renovating properties, view lives auction feed, track the construction of new properties, etc.

5G Network Vehicle Safety and Security

Machine Learning technology is improving the autonomous vehicle experience. How does it work? An automobile can stop when a driver is in dangerous tragic weather or unexpected situation. 

The 5G network can cause the brakes of a car to operate by tracking vehicle sensors of other drivers near prevent or relieve car crashes. 

The network can also send drivers a traffic update to use detours and avoid certain roads that are under construction or is unsafe.

AI and IoT Business Benefits 

1. Guaranteed Security and Safety

A company’s highest priority is protecting data in the workplace. As Artificial Intelligence scans security footage, IoT can close gates or doors if an intruder attempts to enter the premises of a head office. 

Organizations are now using machine-to-machine communication to determine potential security threats with an automated response to hackers or intruders. 

An example of AI and IoT in banking security is the detection of fraudulent activity in ATMs to communicate updates to law enforcement to protect customers.

The unexpected workplace accidents can be prevented by using sensors that monitor safety hazards as employees work. Employees at some organizations now wear wearable devices that alert the management of undetected dangers such as carbon monoxide released into the air on a work site. 

2. Convenient Shopping Automated Experience

Online shopping is more convenient than ever as websites personalize real-time suggestions to consumers based on a customer’s shopping history. As a result of this investment, Kinsta predicts that by 2021, Artificial Intelligence in e-commerce will increase sales to $4.5 billion from $2.3 billion in 2017.

3. Enhanced Healthcare Experience

NovitaCare, a Netherlands based healthcare company that treats patients with chronic and multiple disorders, wanted to improve the caregiver experience using an effective online platform. 

With Fingent’s help, NovitaCare now can communicate with non-profit organizations, patients, providers and researchers with an online platform that is HIPPA compliant.  

4. Simplified Management Of Supply Chain

The supply chain industry has experienced challenges in managing unexpected events that happen due to inaccurate forecasting. A solution to the problem is implementing AI and IoT. 

Supply Chain Digital recently stated the following about these technologies:

“Intel highlights that the world of IoT is growing rapidly, from 2 billion objects in 2006 to a projected 200 billion by 2020.” 

“AI is on most companies’ radars, with 78% of organizations implementing it to enhance operational efficiency by at least 10%.”

The use of real-time devices will feed data to executives to help create contingency plans for preventing unexpected challenges in the industry. As a result, the supply chain and a company’s reputation can experience fewer impacts.

A Guide for AI-Enhancing Your Existing Business Application

How Fingent Helps Businesses Achieve Success With AI and IoT?

Fingent has mastered the art of technology infrastructure to help companies resolve AI and IoT processes. As a result, it creates efficiencies in managing smart devices.

Implementing these technologies are small changes that can have a huge impact on your business. The ability to use raw data to understand customer behavior and forecast trends in the market can improve customer loyalty. Also, companies can track employees working in multiple departments and locations across the globe by partnering with Fingent.

Fingent is confident that AI and IoT work in your business context by delivering technologies to enable solutions in the cloud, networks and gateways, heterogeneous device support, systems capabilities, and data analytics. 

To Conclude 

Business Insider predicts that there “will be more than 64 billion IoT devices by 2025, up from about 10 billion in 2018.” 

Gartner observes that in three years (by 2020), more than 80 percent of enterprise IoT projects will incorporate at least one AI component. Artificial Intelligence and the Internet of Things is used to improve the safety of drivers on the road, enhance healthcare experiences, automate and streamline enterprise processes, stop intruders from hacking into IT systems or large organizations, and in numerous other ways. 

The combination of these technologies not only delivers a superior customer experience, but also forecasts what customers want in real-time, improves their experience of living in smart cities, maintains a high safety rating in challenging workplaces, and reinforces physical and cybersecurity. AI-IoT duo also avoids any unplanned downtime, increases operating efficiency, helps develop new products and services, and improves your risk management. 

Are you looking for an AI and IoT partner? Get in touch with Fingent experts today for a streamlined and error-free IoT implementation for your business.

How Is AI Changing the Game For HR?

With Artificial Intelligence (AI) making sweeping reforms in every aspect of a business, it comes as no surprise that Human Resources have discovered just how beneficial this technology can be. From talent acquisition to policy planning, payroll engagement efforts,  and reporting, AI recruitment is enabling businesses to save time and costs, increase productivity and improve accuracy.

This blog will discuss 3 ways in which AI has proved to be a game-changer in HR.

1. Selecting and Training Employees

Hiring the right employee and ensuring that he is a good fit in the organization is a fundamental tenet of success in business. Let’s consider the hiring process. AI recruitment can surpass biases, eliminate manual and machine limitations, and enhance the screening process by better identifying skills gaps and selecting candidates that have the highest potential for success.

Dr. Jeremy Nunn, Founder, and Director of Workmetrics, a leader in workforce software say: “Thanks to AI, organizations are able to better grade and rank qualifications during the screening process, develop candidate profiles, interact with job candidates and quickly reach out to the best ones before other companies take action.” Consider the hours of work that this will save for recruiters!  

AI can also enhance existing technologies and refine the application process. In an earlier blog, we had talked about how the Applicant Tracking System (ATS) is Transforming Hiring Norms. AI can further enhance these technologies in several ways. For example, instead of the ATS relying only on a set of manually entered keywords to qualify a resume, AI helps the system look beyond the keywords and understand the concept behind the requirement. Deep Learning, Sentiment Analysis, Predictive Analysis, and other AI concepts can be used to enhance the candidate screening and qualifying process. 

AI recruitment technology has also proved its mettle in the training and development of existing employees. It has the ability to get an overview of the entire workforce at once and automatically analyze the skills in each employee. It can then match these skills to the organization’s objectives and provide insights into specific areas for improvement and recommend the most effective training for each employee. 

2. Improve Employee Experience 

Improving the overall experiences of the employee with the company through every touchpoint is now made possible with AI technology. AI can enhance factors that affect an employee’s experience like compensation, communication, opportunities to grow and much more. 

Let’s consider Compensation Benchmarking for example. The traditional benchmarking strategy based on titles doesn’t cut it anymore, because the functions performed under a title is seldom the same across different companies or even across departments. AI recruitment overcomes this issue by processing large amounts of employee-level responsibility data and providing accurate comparisons between employees through machine learning models. This helps businesses identify and accurately compensate employees with similar responsibilities and caliber. 

Another important aspect of employee experience is access to information and communication. Discussing a survey by ServiceNow, their HR Evangelist & Transformation Leader Jennifer Stroud says: “30 percent of survey respondents said they want functionality that mimics Google to easily search for answers to their questions as well as policies and other critical company information.”

Conversational AI for the HR system will help in this regard. AI-powered programs including chatbots can be used to provide employees with instant answers to their questions right from the onboarding process. Access to basic training modules, guidelines on business ethics and conduct and other information can also be provided this way. Chatbots can govern access to documents based on the employee’s title and other factors. Natural language processing (NLP) has been vital in the success of conversational AI. 

Content personalization by using predictive analytics has also been instrumental in enhancing the employee experience by recommending the most suited professional development programs and career paths for each employee. 

3. Critical Decision Making

With data-backed insights and intelligent recommendation engines, AI can help solve persistent HR challenges. By aligning functions of HR to the overall business strategy and organizational goals, AI helps HR managers come to the right decisions that will be beneficial to the organization in the long run.  

In the context of strategic HR decisions, the role of AI in Manpower Planning and Productivity Management is gaining recognition. Let’s consider the measurement of productivity of the Sales Function as an example. Traditionally, this has been measured by the achievement of targets. This, however, rules out considerations of missed opportunity or optimal revenue extracted from the market. AI helps in this regard by providing an overall analysis of the market, the competition and runs comparisons with the skills, attitude, and effort of the sales professional. Drawing this data from CRM, accounting, Geo-position tracking, sales logs and more is no easy task but is achievable by AI. 

Transforming HR with AI

Although two-thirds of CEOs say AI will drive significant value in HR, only 11% of CHROs say that their organization has the skills to implement it. Don’t let your business be one of the 11%. Give us a call and let’s make AI happen for you. 

A Guide for AI-Enhancing Your Existing Business Application