Discover our latest articles

Applications of Artificial Intelligence and Machine Learning in Banking

Applications of Artificial Intelligence and Machine Learning in Banking
With the evolution of technology, computers have evolved from traditional "garbage in, garbage out" models to intelligent systems. Today, computer systems are used for decision making through data analysis and simulation, visual perception, speech recognition, and language translation. From Apple’s Siri, Amazon’s Alexa, Google’s google assistant, to ride-sharing apps; artificial intelligence and machine learning have revolutionized how humans interact with computers. They are among the most trending technologies of this generation.

So what are machine learning and artificial intelligence? Machine learning is the development of algorithms that can learn from data, and utilize that knowledge to offer solutions to practical problems. Artificial intelligence is the study and building of computer systems with an ability to intelligently function. This paper discusses the application of artificial intelligence and machine learning in the banking and insurance sectors.

Application of artificial intelligence and machine learning in the banking industry has transformed every aspect; from faster processes, secured money transfer, and efficient back end operations. The following are the banking aspects that utilize artificial intelligence and machine learning.

Front Office

The front office is a banking aspect that directly deals with a customer. The following are how artificial intelligence and machine learning has been utilized in this area:

Recommendation engines—Artificial intelligence and machine learning are overly dependent on the availability of digital data. Just the way Google assistant utilizes a user's habitual history to serve feeds that are of interest to them, banking recommendation assistant utilizes a user's digital banking fingerprints to profile the user. Using the profile, the recommendation assistant feeds the user with a specialized array of products and promotional offers.

Automated personal assistants and chatbots—Automated chatbots and personal assistants are the norms in this technologically savvy generation. These features are powered by AI through the smart chatbots and voice-controlled assistants. Through these features, a user can be timely afforded assistance regarding their bank accounts or general concerns.

Middle Office

Decision scoring system—Artificial intelligence and machine learning are used in the verification and update of a user's credit card transaction. For example, when a user's credit card is swapped through a credit card terminal, artificial intelligence and machine learning models are used to verify the credit card and transaction, then the transaction is logged and updated at the central bank systems.

Banking loan apps—The primary factor for banks when it comes to the provision of loans is the ability of the borrower to timely payback. With artificial intelligence and machine learning, the borrower's vast digital footprint is analyzed in the determination of the probability of default. All then gathered digital information is collectively known as "alternative data"; lenders are in a position to determine the FICO scores and creditworthiness of a borrower.

Optimization in mortgage operations—Banks routinely processes multiple mortgage-related applications; through artificial intelligence and machine learning, banks can efficiently streamline these processes. Machine learning is deployed to process the data and provide actionable and relevant information. For example, through artificial intelligence and machine learning, a mortgage application can be fairly profiled according to their credit history.

Back Office

Fraud detection—Artificial Intelligence can detect fraudulent activity in real-time; and applying machine learning to available historical data, behaviours of future fraudulent activities can be documented. The documented data on the behavioural characteristics of fraudulent activity is fed into a fraudulent detection algorithm; this practice enables the building of algorithmic models that will be able to filter transactions, flagging off transactions with suspicious characteristics. The more data is available, the more robust and efficient fraudulent detection systems are achieved.

Intelligent trading system—Utilizing available database data and other digital footprints of a bank institution, artificial intelligence, and machine learning can accurately determine the stock performance of an institution by factoring in past data. Through the data, AI can develop an accurate and comprehensive portfolio that captures both the short and long term goals of an institution.

Robotic process automation—Robotic process automation is the employment of software bots to carry out a specific bank operation; the operations are normally data-management-related. RPAs have proven to handle complex and robust operational functions. Tasks such as banking calculations, account matching, and other banking operations that are often at risk of human inaccuracy can be left to RPA to manage. In automation of these intensive functions, banks are guaranteed efficient operations.