Role of AI in Fintech

Financial companies were the early adopters of the centralized computer, relational databases, and have eagerly anticipated the next level of computational power. Inorganics Intelligence enables Fintech firms in solving human issues, by increasing efficiency. AI (Artificial Intelligence) improves results by applying methods obtained from aspects of Human Intelligence at a far off human scale, the computational arms race of the last 20 years has revolutionized the FinTech organizations. Technologies like Machine Learning, AI, Big Data Analytics, Neural Networks, evolutionary algorithms, and much more have permitted computers to crunch huge varied, diverse and deep datasets than ever before.

In the early ages of Banking, bankers used to develop personal connections to their clients to help them assist well for their decisions. But in this digital era, this personal connection has vanished. Would technology be able to bring back the human connection? At various levels, AI in fintech can be utilized to bring back that connection. Machine Learning and Artificial Intelligence can process an immense amount of information about customers. This information and data are compared and results in suitable products/services that customers require. This basically means finding what’s ideal for your customers and hence can accomplish customer satisfaction at a high level.

The Potential use of AI in Fintech

• Accurate Decision-Making

Pocket-friendly prices of data-driven management decisions lead to an alternate style of management, where leaders of the insurance sector and future banking agents will ask the right questions to machines, instead of human experts. Machines will then analyze the data and will find out the recommended results, which can support leaders and their subordinates in taking better decisions.

• Automated Customer Support

Customers facing systems such as voice systems, text chats, or Chatbots can deliver human-like customer service or expert advice experience at a low cost.

• Claims Management and Fraud Detection

Analytics tools gather evidence and analyze data essential for conviction. Artificial Intelligence tools then learn and monitor the behavioral patterns of the clients to identify irregularity and warning signs of fraud attempts and incidences. Claims management can be developed utilizing techniques of Machine Learning (ML) in different stages of the claim handling mechanism. By leveraging AI and handling an immense amount of data in a small period of time, insurers can automate handling mechanisms. It can even fasten certain claims, to decrease the overall processing time and also the handling costs while enhancing customer experience. These algorithms distinguish patterns in the data to help perceive fraudulent claims in the process.  With their self-learning capacities, AI in fintech would then adapt to new undiscovered cases and further enhance the detection over time.

• Insurance Management

Insurance management with Artificial Intelligence systems will automate the underwriting process and use more crude information to make better decisions for the clients. Automated agents can help the online user, in determining the requirements of insurance. Insurance usually comes into the picture after the occurrence of any loss. Automatic underwriting can immensely accelerate the procedure and often deliver expensive tests not necessary by linking several relevant data sets, even external ones that are absent in the medical records. Rather than paying for the treatments that are costly for insurance, it is better to detect the diseases and risks to prevent them. One can subsequently employ the data that was utilized before to access the risks, to then reduce the probability of damages happening to the insured and also for the insurer.

• Automated Virtual Financial Assistants

Automated financial assistants and planners help users in making financial decisions. These incorporate monitoring events, price of stock and bond trends according to the financial goals and personal portfolio of the user, which can help in making recommendations regarding bonds and stocks to purchase or sell. These systems are often called “Robo-Advisors” and are increasingly being offered both by established Financial businesses as well as Fintech Startups.

• Predictive Analysis in Financial Services

Predictive analytics in financial services can directly influence overall business strategy, revenue generation, sales nurturing, and resource optimization. It can serve as a game-changer by enhancing business operations, improving internal processes, and outperforming competitors. Analytics closely works with businesses over a wider range of industries to arrange and assemble the information, analyze it utilizing leading-edge algorithms and technology and briskly deploy customized, prescriptive solutions unique for every customer. Predictive analysis can assist in calculating credit scores and help prevent bad debts.

Predictive analytics utilizes a massive amount of data to discover patterns and predict insights. These results and insights can unveil what will happen next, what the clients are going to purchase, how long your employee may last, etc. Predictive analytics incorporates everything from sophisticated statistics to Data mining.

• Wealth Management for Masses

Digital and wealth management advisory services offered to reduce the net worth of market segments, resulting in decreased fee-based commissions. Smart wallets developed utilizing artificial intelligence to monitor and learn about the behavior and actions of the client. These instruct users to restrain and modify their personal finance spending for saving their expenses.

Application of AI in fintech for Personal Finance

• Digital Financial Advisor/Coach

 Transactional bots are one of the most popular use cases in AI, presumably because the range of applications is so broad, over all industries, at several levels.

In finance, transactional bots can be utilized to offer customers finance advising/coaching services.

Consider them as digital assistants helping clients in navigating their financial plans, spendings, and savings. Such service enhances customer engagement and improves the overall experience of the customer with the financial product they are interacting with.

Digital assistants can be developed with the help of NLP (Natural Language Processing), a type of machine learning model that can process data in the format of human language. A covering of the product recommendation model can be included, permitting the assistant to recommend services and products on the basis of transactions that happened between the algorithm and the human.

Digital assistants can also be utilized in other finance-related situations such as dividend management, transaction limit approaching, term life renewals, and cheque cashed notifications.

• Transaction search and Visualization

Chatbots can also be utilized in banking to concentrate on search tasks.

Managers offer access to the bot to the client’s transactional data, banking transactions, and it utilizes Natural Language Processing to identify the meaning of the request sent by the client. Requests could be related to balance inquiries, general account information, spending habits, and more. The bot then processes the requests and showcases the results.

Bank of America has such a bot, called Erica, as a digital financial assistant for their customer base. The AI-powered bot was quickly adopted one million clients in three months.

The bot offers a user-friendly search of past transactions, empowering clients to search in their historical data for a specific transaction with a particular dealer, avoiding them the hassle of searching for these in each of their bank statements. The bot additionally computes total amounts of credit and debt, a task that clients had to do by themselves on their calculator.

• Client Risk Profile

A significant part of banks and insurance institution’s jobs is the profiling of customers based on their risk score.

AI is a great tool for this as it can automate the categorization of customers relying upon their risk profile, from low to high.

Building on the categorization work, advisors can choose to associate financial products for each risk profile and offer them to customers in an automated way.

In this case, classification models such as Artificial Neural Network (ANN) of XGBoost are trained on historical customer data and pre-labeling information provided by the advisors, which eliminates data-induced bias.

• Pricing, Underwriting & Credit Risk Assessment

Insurance organizations offer underwriting services, mainly for investments and loans.

An AI-powered model can give an instantaneous assessment of a customer’s credit risk, which at that point permits advisors to craft the most adapted offer.

Utilizing AI for underwriting services enhances the efficiency of the proposals made and improves the customer experience as it accelerates the process and turnaround time of such operations.

The insurance agency utilizes AIDA (Artificial Intelligence Decision Algorithm), a particular AI, which is programmed on past underwriting techniques & payouts and can have diverse classifying procedures such as huge loss payout or price.

The application of this technique isn’t cantoned to insurance; it can also be utilized on credit scoring for loans.

• Automated Claims Processes

The insurance industry as we know it functions on a standard procedure, customers subscribe to insurance, for which they have to pay. If the customer has a problem such as a car accident for automobile insurance, sickness for health insurance, water damage for housing insurance, he/she needs to activate her coverage by filing a claim. This process is often complicated and lengthy.

Transactional chatbots can transform the customer experience into a more pleasant process.

Enhanced with fraud detection, image recognition, and payout prediction, the whole client journey is upgraded less friction, fewer costs for the organization, less operational task, background checks, calls, and fewer errors all in all. The whole process takes less time and turns into a seamless experience for both customers and also the staff of an insurance company.

What the bot does to take charge of the whole cycle: it walks the client through the procedure, step by step, in a format of conversation.