Financial Sector is the the heart of any nation’s economy. It is constantly evolving and now with the implementation of AI in Finance, it is working even smoother. Errors are fewer and transactions are faster. Some of the biggest impact made by AI in the finance sector are-
Customized Financial Services
Financial services are expanding with the implementation of advanced technologies like Artificial Intelligence. With AI, finance companies are able to meet the unique and personalized demands of their consumers. With AI in finance, it's possible to create intelligent products that can learn from the customer's financial data and determine what's working for them and what's not, and help them track their financial activities better.
Efficient Management Styles
AI in finance is opening up new avenues for banking and insurance leaders to seek advice. No more are financial experts limited to human opinions in order to make forecasts or recommendations in the field of finance. With AI in finance, these leaders can now ask machines questions that are pertinent to their business and these machines can, in turn, analyze data and help them take data-driven management decisions.
Risk Assessment and Management
Since the very basis of AI is learning from past data; it is natural that AI should succeed in the Financial Services domain, where bookkeeping and records are second nature to the business. Let’s take the example of credit cards. Today, we use credit score as a means of deciding who is eligible for a credit card and who isn’t. However, grouping people into ‘haves’ and ‘have-nots’ is not always efficient for business. Instead, data about each individual’s loan repayment habits, the number of loans currently active, the number of existing credit cards, etc. can be used to customize the interest rate on a card such that it makes more sense to the financial institution that is offering the card. Now, take a minute to think about which system has the capability to go through thousands of personal financial records to come up with a solution- a learned machine of course! This is where AI comes in. Since it is data-driven and data dependent, scanning through these records also gives AI the ability to make a recommendation of loan and credit offerings which make historical sense.
Better Fraud Detection
Financial services providers are inundated with big data, especially unstructured data, starting with phone calls of traders to emails of dealers. However, the approach to surveillance has been very people-centric, through audits and sampling. However, lately, with the rise of AI-based systems, it is possible to analyze volumes of business data and find out how well the internal control systems are operating. Increasingly, financial organizations are adopting a machine learning-based approach to augment their algorithmic rules-based approach towards surveillance and risk management. Machine learning techniques which are constantly learning on the job can keep a few steps ahead of human- and rules-based fraud detection systems.
One of the banking areas that have seen a considerable investment in AI is wealth management. Both incumbents and newcomers are realizing that the digital shift happening in the banking space would affect this sector. Industry heavyweights are acquiring tech start-ups with special focus on automatic analysis of large amounts of unstructured data. The purpose is to detect "typical" behavioral patterns. These experts are hoping to build AI engines, which can provide insights on how to best service their high-net-worth clients. By automating large parts of the wealth management process, they would be able to offer personalized, tax-optimized investments to clients, who have far less in investable assets than what would usually qualify for professional wealth management.