AI and Machine Learning (ML) Driving Next Gen BFSI Industry
  • byAIBridge ML

 

AI-and-Machine-Learning-to-Drive-Next-Gen-BFSI


AI and Machine Learning have added to the ‘Intelligence Quotient’ of the BFSI sector. Going down the line, RPA and Machine Learning shall help banking firms envision and enact creative strategies not only to win and retain loyal customers – but go far beyond in giving a tough competition to their market rivals with the help of technology transformation. Innovation in BFSI has become all the more essential to fight emerging challenges in areas of risk management and data security.

Apart from the core or internal processes, AI will jump in to help banks and financial institutions with specific essential functions – including fraud detection, compliance, and creditworthiness. With the advent of intelligent agents, fintech companies can now rely on new methods of research before rolling out savings product or investment scheme – which had been a tedious task given the vast amount of information to be scrutinized.

Trading Algorithms

A stock investor or stakeholder would define and write a set of rules to sell the shares when prices are high, and buy those back when cheaper, and programming language does the rest. With the advent of Machine Learning in Banking, it has become not only but ideal to do away with thousands of lines of such a code.

Machine Learning in BFSI has helped financial trading houses to make their processes error-free with swift transactions and faster ROI. It has boosted the mileage of processing high frequency trading and stocks of large volumes or price bands with critical fall or rise within minute intervals.

Security and Safety

Till as recently as a couple of years, computer-aided vision for establishing identity and evaluating credentials had still been something novel. Within this span, there is a surge in the adoption of high-end machine learning tools for identity management and executing policy regulations for users and policyholders of financial institutions – thereby increasing brand credibility in the market.

A centralized identity verification system was a bit cumbersome in legacy databases. With the emergence of intelligent systems, banks can now sort, edit, and reproduce information across transactions and tally all-time history with user requests, transactional earnings, and conduct an intelligent pivot or cross-tab to pull essential data. AI algorithms can detect malware and quarantine threats from hackers with the first wall of protection.

Intelligent Financial Advisors

As banking modernizes with the advent of AI, it has eliminated the human roles of financial advisors and relationship managers. With intelligent agents acting as digital advisors, customers can always establish a rapport with virtual assistants. AI can now facilitate the creation of bank accounts with real-time information and credentials processed instantly. With more automated payment methods, users are now powered with options for NFC and QR across banking and financial services.

Machine Learning in Banking has developed bots for a financial institution has allowed the progress of intelligent investment decisions from the customer viewpoint. Machine learning in banking now automates the entire cycle of transactions from inquiry to completion, and meets applicable guidelines along with delivering personalized customer satisfaction. Come 2023, the Robotic Advisors are poised to be valued at three times their market valuation in 2018 – that already stood at $426 billion.

Creditworthiness

Based on payment timelines of customers, Machine Learning in Banking and Insurance has helped financial institutions to establish payment histories with 30, 60, or 90-day cycles. These banks rely on data points on financial histories as well as rudimentary logic regression models to assign creditworthiness. This will help segregate likely defaulters based on intelligent inputs for due diligence – apart from whether the loan or payments to customers can be approved at the first place.

A diverse range of rents and utility bill payments now act as tools to determine creditworthiness. Lenders can use more advanced models that are built upon neural networks. As of 2018, these neural networks have proven more accurate over decision-trees or logistic regression models.

Stock Performance Forecasting

Financial pundits are increasingly relying on AI to forecast the performance of stocks. As opposed to humans, the use of intelligent machines will allow skewing of previously charted stock performance to determine the performance of stocks during specific futuristic time-intervals. Data generated with machine learning on regular time-periods offers a clear insight to the anticipated customer spending along with sentiment analysis on a range of available insurance products and go-to-market stock options.

Foreign exchange and currency valuations have come a long way in leveraging Machine Learning in Banking and Insurance. The trend extends to intelligent hedge funding options with focus on analysis of social media posts and tweets that also help gauge sentiment analysis. Machine Learning in BFSI models can thus track the overall impact on market from hedge funds with required segmentation.

According to a study, JPMorgan Chase has devised intelligent algorithms that perform in sync to offer volume-weighted and time-weighted average pricing and regular currency values based on prevailing market variables or real-world events. Global banking firms are pursuing all the more new ways of making profits with Data Analytics solutions driven by Machine Learning.

Chatbots in Financial Services

With the help of Machine Learning in BFSI, Chatbots can be trained to handle queries and advise or suggest appropriate solutions. It also helps in managing overall customer satisfaction of customers with financial products or investment plans they have made. 

Machine Learning can convert feedback from customers with the help of Chatbot data to gauge market pulse for introducing more attractive schemes. Chatbots are likely to replace humans for advising on the most feasible financial products and services for customers.

Fraud Detection

The volume of rising electronic transactions has also added to the risk of fraudulent activities. There is a need to protect and secure virtual transactions and add to the credibility of payment gateways. BFSI firms and payment merchants are increasingly adopting machine learning to detect frauds with intelligent mechanisms.

Machine Learning in Banking also enables deployment of predictive solutions based in intelligent algorithms to detect and report fraudulent activity across online or offline channels or modes of payment. It intelligently analyses high-volume application data with that of personal credentials of the customer in all possible virtual scenarios.

Growth Forecasting

Machine learning in BFSI can be of great use to fund managers. They can potentially identify the growth rate of their investment scenarios based on the changing market conditions in advance. This will help in analyzing future-looking statements and potential in areas like investment banking.

Digital assistants will also provide key finance metrics for decision makers based on predictions. These forecasts from machine learning will also help automate decision making and possibly help machines rather than humans - in taking decisions based on forecast data.

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