Tag: Artifical Intelligence

Top Artificial Intelligence Strategies For Investment Management

Artificial Intelligence can prompt you what best to invest in. Digital Revolution is at its height and AI implementation makes use of digitized data to help businesses make decisions based on past performance.

It is a fact that 80 percent of the companies already have artificially intelligent systems implemented. Companies rely on artificially intelligent machines to make investment decisions by analyzing the vast amount of structured and unstructured data at hand. It is necessary to understand how artificial intelligence fosters to enhancing communication, forecasting future events, and in making good decisions.

AI helps in investment management by helping businesses augment the intelligence of human workers to develop new technologies. Some examples where AI is employed by investment management teams of businesses are as follows:

• Product Innovation and R&D derive 50 percent of its revenue from AI.
• Supply Chain derives 42 percent from AI.
• Customer Engagement Services to around 46 percent revenue form AI.
• Sales deriving 34 percent.
• Security of about 40 percent.
• Data Gathering derives to about 27 percent of its revenue from AI.
• Intelligent Workflow and Automation around 23 percent.
• Operations on Analytics by 20 percent.

Related Reading: Check out how artificial intelligence is revolutionizing small businesses.

The potential of Artificial Intelligence in managing assets is significant. So how can the investment management business derive its maximum out of AI? Here’s how:

Machine learning is used in investment management to learn from large volumes of structured and unstructured data. This data is gathered from past transactions and is used to predict buying and selling decisions.

According to JP Morgan, the investment management industry is likely to spend $2-3 billion on BigData. BigData is modeled and distributed in order to generate insights by performing predictive analytics. It also ensures high investment returns.

Machine Learning takes huge chunks of unstructured and raw data. This data is then organized and streamlined into predictable patterns. Based on these patterns, deep analysis is conducted and critical insights are drawn.

Benefits Of Artificial Intelligence In Investment Management

To augment the intelligence of humans, Artificial Intelligence benefits Investment Management in the following ways:

1. Provides Critical Insights

Firms provide their structured and unstructured data sets and derive critical insights from the patterns generated. Historical data provide insights that help in making investment decisions for firms. This also helps in predicting future business outcomes.

2. Reaching Out To Clientele

Artificial Intelligence helps in automating processes. For instance, getting an email or automated feedback or notification on how to manage investments is performed by analyzing historical data via machine learning algorithms. AI helps in reaching out to the clients with the help of monitoring search engines and crucial insights drawn out of patterns from historical data.

3. Reporting Services

Natural Language Generation or the NLG technology enables the automatic analysis and explanation of data and transforms the data-driven sections into reports. These reports are generated for clients. Marketing material, portfolio, and many more such tasks use the NLG technique.

4. Better Customer Experience

Chatbots and machine learning to serve the investment management industry verticals to a great extent. For instance, there are smart chatbots that offer guidance for managing investors in taking critical decisions. Natural Language Processing techniques are a rapidly growing use case in the investment management domain.

Related Reading: AI-powered Chatbots can help you redefine your business. Read on to know how!

5. Enabling Risk Management

Machine-learning algorithms can transform the existing validation frameworks in order to mitigate risks. AI can transform the financial sector by making use of chunks of data available to build models. These data models can improve decision making, can manage risks. The McKinsey Global Institute researches state that implementation of AI and machine learning algorithms can generate over $250 billion in the banking sector.

The compliance and risk management functions enhance the investment firms in three main ways as follows:

1. Data Analysis is automated, largely into systems.
2. Tasks such as administrative activities are considerably reduced.
3. The workforce can focus on critical and value-generating tasks rather than having to spend time on redundant and time-consuming activities.

These algorithms monitor for fraudulent transactions and trigger automated responses that can help investors make accurate decisions.

Related Reading: Can Machine Learning predict fraudsters? Read along to know more!

6. Automating Functions

The aggregation revolution that AI has gone through in recent years has fueled the pace at which critical data reaches investors. Investment management is now going through an analytical revolution where artificially intelligent systems and investment is coupled together to transform the approach in analyzing data, how the data is packaged and how this data is showcased to the investors.

This augmented intelligence will help potential investors to professionally be informed about crucial investment ideas and also regarding stock selection and portfolio construction tools.

Related Read: Artificial Intelligence: Taking The Buzz Out Of Buzzwords

7. Taking Over Tedious Tasks From The Workforce

Artificial intelligence can perform screening of stocks, in addition to being able to rapidly identify stock opportunities among global markets. This is a huge preference for investors to be able to identify accurately the various means of shifting tedious tasks from the workforce. This is made possible by letting investors know about it via a visual representation of key factors derived from the AI engine.

8. Real-time Visualization And Control

Investors are prompted and notified with suggestions that mitigate the risk of unwanted exposure of critical data and decisions to the outside world. Exposing portfolios to unknown sources can lead to frauds. For instance, letting out the details of forecasted details of interbank lending rates can lead to uncertain risks. Thus, risk control is necessary and is achieved via real-time visualization made possible with AI tools.

9. Reducing Investor Biases

Smart monitoring systems powered by Artificial Intelligence are critical because the AI engine alerts the investors regarding factors such as price declines beforehand. This reduces the exposure of the investor to risk factors. It reduces investor biases considerably and creates a more balanced perspective for investors to increase performance.

10. Generating Alpha For Value Creation

Investment firms that require growth, in the long run, can enable AI-based systems, where big data plays a major role in generating alpha. This excess return measures the market’s overall risk volatility and is also termed as systematic market risk.

It is critical for the investment management industry to make accurate decisions and to allocate capital in the right way for enhanced value creation. The four pillars analyzed for a successful transformation thus, are:

• Alpha Generation
• Increasing the Operational Efficiency
• Understanding Investor Preferences in Real-Time and
• Risk Management.

These four pillars are augmented with artificial intelligence to obtain efficient business models. Investment firms that capitalize on these four pillars reap successful business outcomes. Drop a call to our IT strategists right away to gather tips on investment management!

Artificial Intelligence (AI) is considered to be one of the most significant disruptive technologies today. More and more businesses are already realizing its benefits. Gartner’s 2019 CIO survey revealed that the percentage of companies implementing AI increased by about 270 percent over the last four years, and 37 percent in 2018 alone.

Leveraging the power of AI to enhance your existing business applications isn’t nearly as complicated as you might think. You don’t need a billion-dollar budget to implement AI-powered applications. In fact, small and midsize businesses (SMBs) today are cutting costs and delivering great customer experiences with AI-powered applications—and they are competing with giant companies at scale.

Here’s a look at how you can enhance your existing business applications with AI:

Enhance CRM Apps with AI

Incorporating AI into your current Customer Relationship Management (CRM) system, for instance by using chatbot or automated live chat support, will allow your company’s helpdesk to provide better, faster and more dynamic responses. It will also help you reduce the man-hours needed to resolve queries and help you build better engagement and customer trust. And because the AI-powered CRM system provides predictive insights, you can automatically recommend similar products or services a customer may be interested in.

Related Reading: Unconventional Ways Artificial Intelligence Drives Business Value

Streamline Supply Chain with Machine Learning

Machine learning (ML) allows your system to discover patterns in the supply chain data using algorithms that automatically identify the factors that contribute to the success of your supply networks, while constantly learning in the process. ML algorithms and the applications running them can analyze large, varied data sets in no time, improving accuracy in forecasting supply and demand. If applied correctly within your SCM work tools, ML could revolutionize the agility and optimization of your supply chain planning.

AI-Powered Recruitment Apps

Artificial Intelligence is expected to replace 16 percent of Human Resource (HR) jobs within the next 10 years, according to Undercover Recruiter. Integrating AI into your existing recruitment processes or tools could help your company’s HR department find the right candidate or the best fit faster and easier, thereby saving you time and money. AI-powered video interview tools, for instance, can utilize biometric and psychometric analysis to evaluate your applicants’ tone of voice, micro-expressions, and body language.

Related Reading: AI To Solve Today’s Retail Profit Problems

Improving Cybersecurity System with AI

Given the data breaches and cyber-attacks that have hit headlines in recent years, integrating AI into your current security system is vital to protect consumer data, improve trust and deliver true business value. About 71 percent of companies in the US plan to spend more budget on AI and machine learning in their cybersecurity software this year.

AI not only improves your company’s existing detection and response capabilities but also allows new abilities in preventive defense. It enhances and streamlines your security operating model by reducing complex, laborious and time-consuming manual inspection and intervention processes. Because the AI-powered cybersecurity system can self-adjust and learn data over time, you can automatically detect and block cyber-attacks and fraud.

Enhancing Space Exploration with AI

Another area where the application of AI has great potential is exploring outer space. NASA has plans to look for life on other planets, such as Mars, in the very near future. In their Mars 2020 initiative, they will use AI to explore Mars in greater depth, which includes looking for alien lifeforms. Most of us are at least slightly familiar with or aware of NASA’s Opportunity rover, which wrapped up a 14-year Mars mission when it quietly went dark in February 2019. Opportunity, also known as “Oppy,” found evidence that Mars at some point was home to water — a huge discovery.

Going forward with Mars 2020, NASA’s Mars Exploration Program will continue its use of AI for space exploration. In ongoing efforts to evaluate whether Mars is (or was at some point) habitable for humans and other animals, the Mars 2020 rover is equipped with a drill it will use to collect samples of rock and soil. It will store these samples in special tubes that will be collected by a later NASA mission. Read more about the artificially-intelligent robotic arm that will make it all happen.

Related Reading: Industry experts weigh in on the adoption of AI and ML in software development

Taking Your Existing Business Applications to the Next Level with AI

New AI frameworks and tools make provisioning AI capabilities more feasible than ever before. Working with a development partner who has the data science and AI technology experience, creating or updating a business application with AI can be started rapidly, take less time to code, and the resulting application placed into service sooner. Nor would it be necessary to staff for these hard-to-find resources for the long term.

Related Video: Artificial Intelligence – How to navigate AI

The year 2017 has been eventful for digital transformation, with technologies such as Big Data coming of age. Digital transformation promises to be more disruptive in the coming years, as evident from the following predictions.

1. IoT Becoming Mainstream

The much-hyped IoT will walk its talk fully in the future. About 8.4 million “things” are already part of the IoT ecosystem, a 30% increase from last year’s levels.  Enterprises will start using IoT to deliver better products, services, and insights. IoT will permeate to the masses, and become mainstream and commonplace.

Among specific sectors, IoT will revolutionize analytics in a big way, driving new smart solutions. A few possibilities include hyper-efficient fleet operations, intelligent traffic signals, and more. Tech giants such as Microsoft, IBM, SAS, and SAP are all heavily investing in IoT Analytics, offering a portent of the things to come.

2. Edge Computing Offers Effective Real-Time Processing Solution

The cloud and Big Data analytics analyze tons of data seamlessly. However, the sheer volume and velocity of data create time lag and some inefficiency. For instance, the cloud, for all its advantages, is not viable for IoT powered smart drones, autonomous vehicles, and other AI-powered smart devices. These devices need real-time and instantaneous data processing. Sending data from these devices “all the way” to the cloud is an inefficient and impractical method of processing data.

Edge computing, which performs data processing at the edge of the network, near the source of the data, promises effective solutions to the shortcomings or limitations of the cloud and big data. Edge computing, for instance, allows IoT powered devices to connect and communicate instantly and seamlessly.

Industry biggies, such as HPE and CISCO have already rolled out hardware and software to actualize Edge computing. IDC predicts 40% of all computing to happen at the edge in the next couple of years.

3. Enter the 5G Network

4G or fourth-generation wireless, synonymous with LTE (Long Term Evolution) technology, went mainstream in 2017. 5G of fifth-generation wireless will go gain ground in coming years. The need for hyper-connectivity and IoT propels the need for 5G.

The Next Generation Mobile Networks (NGMN) Alliance defines 5G as “an end-to-end ecosystem to enable a fully mobile and connected society.” 5G is not an incremental upgrade over 4G or LTE.

While the 4G focus is on raw bandwidth, 5G focuses on pervasive connectivity and super-dense network, enabling even faster and resilient access to the Internet even from the remotest caves or desolate hills. Unlike the hitherto monolith networks entities such as 2G, 3G or 4G, 5G co-opts a combination of technologies, including 2G, 3G, LTE/4G, LTE-A, Wi-Fi, and more. While emerging technologies and solutions, such as IoT, connected wearables, augmented reality and immersive gaming places a great strain on incumbent networks, 5G will run these technologies seamlessly.

Industry majors such as Sony and Samsung are investing in Gigabit LTE, the stepping stone between the incumbent LTE and 5G. The already well-entrenched Qualcomm Snapdragon technology powers Gigabit LTE. However, 5G will become a household technology only after a few years.

4. Blockchain finds Its Way

Blockchain, the secure transaction ledger system distributed across a network of computers, rather than under any single entity, will finally become mainstream by 2018. The financial industry has already started to embrace blockchain in a big way. The healthcare, entertainment, and hospitality sectors are on the verge of embracing it in a big way. Dubai is rapidly moving toward becoming the world’s first-ever blockchain-powered government by 2020.

5. Artificial Intelligence Becomes Mainstream

The market size for AI is set to double up from $2420 million in 2017 to $4066 million by 2018.

Solutions powered by Artificial Intelligence are already popular. Artificial Intelligence already powers many popular solutions, such as Alexa, Siri, Salesforce Einstein CRM, IBM Watson, SAP Leonardo, Netflix, Amazon AI, Cortana Intelligence Suite, and various customer service chatbots.

Companies will continue to roll out cutting-edge solutions based on Artificial Intelligence, especially to implement smarter and cheaper automation. The scope of artificial intelligence would expand to encompass everything from emails and content generation and from industrial manufacturing to smart grids. In fact, major companies will embed Artificial Intelligence into their core operations.  Fueling the growth of Artificial Intelligence is the spread of open-source solutions. Artificial Intelligence cannot easily be integrated into closed systems.

With more investments being made betting on Artificial Intelligence, Virtual Reality is losing the race significantly because Artificial Intelligence offers whatever Virtual Reality offers, in a much cheaper and better way. For instance, companies can apply 3-D visualization to train, pitch, and envision new products in a much better way than what Virtual Reality offers.

6. Anything as a Service

XaaS or “Anything as a Service” is now within the realms of possibility. The cloud-based services market now encompasses software, infrastructure, and everything else. The latest to enter the “as a service” market is a framework!  Framework-as-a-Service (FaaS), which falls between SaaS and PaaS is a customizable cloud-based platform. Users may indulge in rapid prototyping, visualization, and other fast fail methods to discover whether a concept or strategy will work or not. Companies get to know the result of their initiative, without having to spend time and effort, doing it the hard way.

Side-by-side with FaaS, workplace-as-a-service (WaaS) and unified-communications-as-a-service (UaaS) will also become mainstream in the future. Remote workplaces, powered by WaaS, will rise in a big way in the future.

7. Low Code Platforms to Soar in Popularity

Low code development platforms (LCDP) will net a total revenue of $6.1 billion by 2018, and over $10 billion by 2019. These figures are impressive when compared with the total revenue in 2015 which was just $1.7 billion.

LCDPs allow creating apps through a configuration of functions, and intuitive drag-and-drop options, rather than by hand coding. The obvious advantage is ease of development and accelerated delivery of business applications. Acute shortage of talented programmers fuels the growth of LCDPs.

Some popular LCDPs, as of now, are Appian, Mendix, Google App maker, and Zoho Creator. The growing popularity of such low-code development platforms will spur ‘citizen development’.

Technology is always fluid. Companies who embrace the latest technology head-on to deliver better solutions for its customers stand to reap dividends. Companies who do not change will surely be swept away into obsolescence.