Use of Artificial Intelligence and Machine Learning in Financial Services

Use of Artificial Intelligence and Machine Learning in Financial Services

Believe it or not, Sci-Fi’s are intriguing and often way ahead of their time. Take, for instance, the 1999 hit Matrix, an entire reality system created by powerful computers. A concept that was simply wow! And now in the 21st century, it has started to become more of a reality. Machines have evolved, are capable of learning from patterns and giving suggestions and doing much more. And all of that falls under the cap of Artificial Intelligence (AI). By definition, AI is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions) and self-correction.

Particular applications of AI include expert systems, speech recognition, and machine vision. The very applications of Artificial Intelligence and Machine Learning in Financial Services are becoming popular today. While going Global has been one of the priorities, staying on par with the current technology standards and providing customers the best financial offerings and advanced services has pushed organizations further in. In terms of mobile payments, internet finance, and P2P lending, Chinese Fintech companies have been trendsetters. Here are a few ways in which we can use Artificial Intelligence and Machine Learning in Financial Services.

Fraud Detection

Fraud Detection

After the global financial crisis, norms have only become stricter and fraud detection a critical necessity. Machine Learning is now being used in Financial services to provide better security to the customers by studying the pattern of financial transactions, amounts, location, time of transaction, payment gateways, etc. to detect fraudulent transactions. Early in 2018, HSBC started using AI (designed by a UK startup Quantexa), to pick up on fraudulent activities and it has seen a significant cost reduction in operations. FICO offers solutions for fraud, compliance, and cybersecurity backed by advanced analytics, artificial intelligence and machine learning technology.

Personalized Customer Service

Customer service is the key to retaining clients and keeping the finances rolling, the aspects without which the global business and finance sector may come to a standstill. The Millennial Disruption Index reports that “73% would be more excited about a new offering in financial services from Google, Amazon, Apple, PayPal, or Square than from their own nationwide bank.” Many of them are known to say, “I don’t see the difference between my bank and all the others.” Many Fintech startups have hence been establishing a clientele by utilizing AI technologies like Neuro-Linguistic Programming (NLP) to provide better and instant service. AI interfaces like Cleo, Eno, and Wells Fargo chatbot, that deliver information instantaneously.


The global RPA market is expected to reach USD 8.75 billion by 2024, according to a report by Grand View Research, Inc. Robotic Process Automation (RPA), can automate the mundane, iterative tasks normally handled by humans. The underlying process of underwriting in banking organizations or actuary services in insurance agencies involves understanding the potential customer’s history in terms of financial transactions, credit scores and payment transactions, a process that can be done faster and more accurately using Machine learning, through the analysis of unstructured data such as social media interactions, phone usage and modes of payments of utilities. Romanian Unicorn UiPath is a pioneer in this area and offers banking automation to various clients across the globe.

Managing Insurance Policies

Just like banking services, insurance organizations to have many processes that can be speeded up through automation. Right from the underwriting process to sorting through huge amount of data to identify cases that can be of higher risk, AI offers great scope in terms of improving profitability. If reports are to be believed, among the Insurance leaders, “85 percent believe it will be critical to the future of their business and, as a result, 96 percent said they intend to invest in cognitive capabilities.” AI algorithms enable better modeling and create better formulas for improved customer service and product development.

US startup Lemonade used a bot called ‘Jim’ which took less than 3 seconds to settle an insurance claim by executing multiple back-end processes simultaneously. Another US startup called MetroMile is known to be using AI to develop an entirely new business model where the insurance premium is calculated based on usage by installing an IOT device on cars to gather data about the user.

Regulatory Compliance

Compliance is one of the top priorities for financial service providers. According to reports, “The London-based HSBC Bank spent $2.2 billion on regulation and compliance in the first nine months of 2015, up 33% year on year. And The annual spending by financial institutions on compliance is estimated to be in excess of US $70 billion.” Compliance is a tedious process and very often sorting through the pile of legal paperwork takes more time than actual implementation, hence slowing down the entire process. AI can reduce the time spent on analysis and also assist in calculating and analyzing cash flows and predicting plausible scenarios. One such example. A program utilized by JP Morgan called the Contract Intelligence, COIN. It is a machine learning system that completed 360,000 hours of compliance work in seconds and also managed to help JP Morgan decrease its number of loan-servicing mistakes.



The biggest power that any financial organization holds is the ability to predict, to foresee financial changes and to provide advice to the customers so as to safeguard their assets. In recent years, AI and Machine Learning have been increasingly put to use to provide automated investment advice along with brokerage and investing services. Robo-advisors utilize machine-learning algorithms to discover or identify and manage ETFs (exchange-traded funds).

AI and Machine Learning are also gaining traction in Wealth and Asset Management. It doesn’t come as a surprise that, organizations like Blackstone, S&P Global and Euronext are adopting machine learning in finance to improve forecasting and increase value for their clients. AI in the Fintech Market 2019 research report states that “The AI in Fintech market is said to have a potential scope for growth in the years to come due to the changing technology, which is improving the business processes of financial service providers. The growing internet penetration and availability of spatial data are some of the major driving factors for the market.”

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