For a long time, traditional banking systems have been relied upon by the financial sector blindly. However, the rapidly advancing landscape of technology has changed customer preferences and expectations. This has made banks and financial institutions reconsider their strategies to modernise their systems. Amidst all this, the financial technology or the Fintech industry has emerged as an eminent hope for the financial sector. One of the major players that have been most effective in the progression of the Fintech enterprise is artificial intelligence. The technology has been implemented for several use cases in the financial industry. Analysts often go so far to say that without AI there is hardly any future for Fintech. Some of the best AI application that the Fintech industry has developed are:

Insurance Claims Processing

Much of the financial sector comprises of the insurance business. Processing of claims is often a lengthy procedure that is frustrating for both the clients as well as the provider. For filing an insurance claim, the client has to answer a series of complicated questions. With AI, however, processing of claims has been automated process. A chatbot is responsible for processing everyday claims for clients. The bot asks the client a standard set of questions that can enable the filer to fill out his/her claim. This process is further improved with other tools like image processing and fraud detection. This enables insurance companies or banks to streamline the process of filing claims and increase both customer satisfaction and efficiency. It also reduces operational and other costs for them.

Financial Management

Another amazing tool that the Fintech sector has been able to develop is automated wealth and financial management systems. Virtual assistants are not entirely new to customers. A financial advisor works on the same concept and answer questions and offer advice to customers according to their bank account history. They are able to give savings and investment advice based on client spending patterns, total income and savings. Natural Language Processing (NLP) is used to develop these digital assistants that are also able to provide recommendations to clients regarding the financial products or services they should use. One example of this is the digital assistant Erica, developed by Bank of America, that offers comprehensive advice and assistance to customers around the clock.

AML Compliance

Banks and financial institutions in the US alone spend nearly $25.3 billion annually on their compliance systems. This, coupled with the reality of today’s regulatory environment, has made the fulfilment of compliance requirements a challenge for financial service firms. However, Fintech has come up with smart solutions for this problem as well. Digitised systems are now available that can help implement anti-money laundering (AML) compliance systems effectively with much fewer expenditures. Systems like behavioural analytics help in anomaly detection, allowing banks to detect fraud or suspicious activity in a client’s account. Real-time AML screening for Politically Exposed Persons (PEPs) in global sanction lists has made onboarding procedure much easier for both the clients as well as financial firms. With the help of AI, banks can implement AML KYC compliance without spending outrageous sums.

Intelligent Contract Reviewing

The financial services industry has to review hundreds and thousands of contracts on an annual basis. It is a mundane and monotonous task that requires thousands of billable hours from lawyers and analysts to be spent on. Nearly two years ago, one of the largest banks in the US – JP Morgan invested in an AI-based technology that uses OCR or Optical Character Recognition for reading documents. It further uses an NLP system to analyse and interpret the documents. This has enabled the banking giant to free up 360,000 labour hours, thus effectively saving them substantial costs and speeding up the process as the system takes mere seconds to process the contracts. Such systems if developed widely can enable other banks and financial institutes reduce their costs effectively all the while reducing the time for processing and reviewing lengthy but repetitive contracts.