Integration of AI in Financial Services
Over the last few years, Artificial Intelligence has become a force to reckon with. Its indispensability can be directly associated with the impact it has been creating across industries. From personalised services according to the individual customers to processing volumes of data to predict occurrences, AI’s efficiency has been proven time and again with how it has improved our day-to-day lives.
According to the NASSCOM report 'Implications of AI on the Indian Economy,' on average, a unit increase in AI intensity by companies that use AI can contribute US 67.25 billion dollars or 2.5 per cent of India's GDP to the Indian economy in the near term. Additionally, according to the recent IDC Forecasts, AI Services is forecast to grow 17.4 per cent year over year in 2021 worldwide, outperforming the overall AI market.
The potential of AI is so promising that we now have celebrities gracing our television screen endorsing the idea of a digitised banking system to make it more secure and seamless. Its awareness has been spread far and wide and the rise in digital technologies has only made consumers more inclined towards having a virtual experience in financial services. With the pandemic accelerating digitisation across sectors, customers now want easy access to products, services, and knowledge to have a hassle-free experience. More than just customer satisfaction, the integration of emerging technologies in the financial services sector has manifold benefits for banks, NBFCs, and fintech platforms. According to a survey by Deloitte Insights, 70 per cent of all financial services firms are using machine learning to predict cash flow events, fine-tune credit scores, and detect fraud. From processing of data to contactless banking, algorithms in emerging technologies are fast becoming the key drivers of growth for financial institutions.
With the increasing credit economy of India and financial platforms penetrating the deeper pockets of the country, there are millions of people who are looking for new credit opportunities every month. This vast pool of prospective customers can only be tapped by smart and suave credit rollout decisions by lending institutions by accurately assessing the customer behaviour, borrowing patterns, and their credit score calculation.
For predictions on customer behaviour or recommendations for customers availing financial services through traditional or new age banking systems, markets are creating models with the help of AI which utilise existing data for customer retention, acquisition, risk assessment, resource utilisation, and knowledge sharing for effective planning and decision making.
The willingness of consumers to become financially independent has prompted them to seek advice on investments, expenditure, and better management of personal finances. Artificial intelligence can help service providers with customer segmentation based on demography and sociography and recommend them plans accordingly which will help them have better control of their finances. Even churning out advice from platforms such as traditional banking institutions can be done based on consumer’s response to knowledge seeking. This is certainly a necessity for players looking to stand out from the others.
The pandemic has also brought forth the gaps in the security systems of financial institutions. According to RBI’S Annual Report of 2020, Public sector banks—that witnessed a 234 per cent year-on-year rise in fraud cases—accounted for 80 per cent of the total such reported instances. Private banks, which reported a more than 500 per cent rise, formed over 18 per cent of the total fraud cases. AI’s intervention will identify these gaps and reduce the possibility of fraud significantly. Even cyber frauds in financial institutions can be blocked through artificial intelligence by identifying suspicious activities or malicious bugs in the system. Pattern irregularity can also be identified by AI which can prove useful for banking services.
For new-age customers who have a higher disposable income, artificial intelligence can be used to address their needs. These customers prefer using financial services virtually which is hassle-free and effective and would ideally prefer being recommended products through the platforms that they are using extensively. Tapping into the platforms that they are active on, their approach towards monetary transactions, etc. can only be derived from strategic use of Artificial Intelligence.
With the mass movement towards digital banking systems, privacy concerns, and getting the best value for money in investments, data processing is a necessity that can only be achieved through technological implementation. The use of predictive analytics is also helping banks or financial institutions tap into new markets, identify new opportunities and gain customer trust. The implementation of AI can formalise the financial services industry, especially lending, credit, and other disciplines. It can also enhance the performance of human resources by helping them serve customers better and generate effective outcomes for their organisations.
Impact of AI in Banking Sector
Fraud Prevention
In recent years, fraud cases are growing exponentially in the country due to the increase of e-commerce and online transaction, and AI-based fraud prevention solutions are extremely effective against it. AI-based fraud detection systems analyse clients’ behaviour, location, and buying habits and trigger a security mechanism when something seems out of order and contradicts the established spending pattern. Anti-Money laundering is another area where banks are employing AI/ML-based solutions to reveal and prevent the cases by recognising suspicious activities in real-time and help to cut the costs of investigating the alleged money-laundering schemes.
Credit Scoring
The credit score provided by AI is based on more complex and sophisticated rules compared to those used in traditional credit scoring systems. It helps lenders distinguish between high default risk applicants and those who are credit-worthy but lack an extensive credit history.
Digital banks and loan-issuing apps use machine learning algorithms to use alternative data (e.g., smartphone data) to evaluate loan eligibility and provide personalised options.
Personalised banking
AI-powered smart chatbots and voice-controlled virtual assistants like Amazon’s Alexa provides clients with comprehensive self-help solutions while reducing the call-centres workload by boasting an auto-learn feature. These solutions help in checking the balance, payment schedule, account activities, etc. Many applications offer personalised financial advice to help the individual achieve their financial goals by tracking income, essential recurring expenses, spending habits and offer the optimised plan and more personalised financial tips. AI-enabled mobile banking apps to provide clients with reminders to pay bills, plan their expenses, and interact with their bank in an easier and more streamlined way, from getting information to completing transactions.
Process Automation
AI, RPA and Intelligent OCR based solution makes it possible to automate a variety of mundane, time-consuming tasks that used to take thousands of work hours and inflate payrolls. It helps verify data and generates reports according to the given parameters, reviews documents, and extracts information from forms (applications, agreements, etc.) to make life easier. Financial institutions can automate processes like trade finance, commercial lending operation, compliance and risk management, letter of credit, cash management, transaction monitoring, and many others.
Anti-Money Laundering (AML)
When fed current payment data, AI solutions can identify fraudulent patterns and alert staff to block these payments. AI can analyse the source and destination of payments, among other factors, to identify deviations from normal behaviour and report for necessary action.
Achieves Regulatory Compliance
Businesses that are competing in high technology industries these days need to stay in compliance with policies and regulations that protect key technologies as well. AI-based scoring techniques are used to keep businesses in compliance with these business policies and rules thus enabling them in fraud prevention. AI/ML-based applications use rule-based algorithms to analyse patterns and predictive analytics to block fraudulent transactions thus helping finance companies in preventing financial frauds.
The author is GM & Head- Artificial Intelligence Platform, NEC Corporation India
DISCLAIMER: Views expressed are the author’s own, and Outlook Money does not necessarily subscribe to them. Outlook Money shall not be responsible for any damage caused to any person/organisation directly or indirectly.