How AI and machine learning are improving the banking experience

Posted by narayana on February 13th, 2020

Artificial intelligence and machine learning are said to revolutionize the financial world, changing the banking experience for the better. The implications of the technology are vast, though most banks are still in the early stages of adopting AI technologies.

A survey by Narrative Science and the National Business Research Institute found that 32% of financial services executives confirmed that they are already using AI technologies such as predictive analytics, recommendation engines, and voice recognition.

One major hindrance to AI adoption is legacy systems. Since banking is a more traditional industry, leaders are reluctant to upgrade or change current technology processes. The problem is these legacy systems often prevent seamless integration of AI.

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With the surge of financial technology (fintech) companies, however, banks need to use technology to remain competitive. Consumers want more from banks, and AI can help deliver on that. And machine learning, a subset of AI, is dynamic and allows banks to rely less on human experts, which means employees can focus more on improving the customer experience.

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While there are numerous implications of machine learning in the banking experience, here are five areas where the technology will improve it substantially.

Credit decisions

AI-based credit scoring can utilize more sophisticated rules than traditional credit scoring processes. It can allow for a fast, accurate assessment of a potential borrower, for far less cost than traditional methods. Furthermore, using technology eliminates bias, as machines have more objectivity than human employees. Banks can determine which applicants are higher default risks and which applicants are more credit-worthy, even without an extensive credit history.

Banks have a lot of data on their customers, including a vast amount of historical data. Data scientists can train machine learning models that perform credit scoring over and over again to learn from mistakes and improve themselves continually. The result is a faster, more accurate credit scoring system that banks can trust. Consumers receive faster responses from institutions and can better understand their finances.

Risk assessment and management

By automating credit risk testing, banks mitigate risk because they receive accurate reporting, not prone to human error. AI does even more to reduce risks for banks and customers. By viewing the history of risk cases, AI can help banks forecast issues and take early steps to avoid problems.

Algorithms reduce risk assessments to minutes, as they can analyze enormous amounts of data that humans cannot do in a short amount of time. Big data can also help individual portfolio holders assess risk so that they can make better financial decisions.

Fraud prevention

Fraud is an element that plagues almost every financial institution, but it’s one where AI and ML have a substantial impact. By analyzing spending patterns, location, and client behavior, machine learning can detect anomalies in spending and alert the cardholder, dramatically reducing credit card fraud. This type of precision not only is impossible for a human to complete, as no one can analyze hundreds of transactions simultaneously and in real-time, but it’s far more precise from an algorithm that is not prone to error.

The system can flag suspicious behavior and either require additional information from the user or block the transaction completely within seconds. This ability means that banks can catch fraud in real-time instead of waiting for it to happen and taking steps to rectify the situation.

Personalized approach

Financial institutions can offer a more personalized experience, thanks to AI and ML. While consumers and businesses want a safe, low-risk approach to financial management, they also value unique experiences and better banking options.

For example, machine learning algorithms can analyze individual consumer data and monitor anomalies. Capital One’s assistant, Eno, for example, notifies members if their card was charged twice for an expense or if they tipped an exorbitant amount at a restaurant. These frustrating hassles are alleviated from the consumer, who can simply notify the assistant whether or not the transactions were valid. Machine learning models can also predict which banking tools individual members might use and recommend them so customers can make better financial decisions.

All of the big banks offer reminders to pay bills, financial planning tools, and other perks that make finances easier to understand and track. Thanks to AI, these personalized experiences can help keep consumers happy and loyal.

Process automation

Automating repetitive and mundane tasks frees up resources and capacity to provide better service to customers. Using robotic process automation (RPA), banks can remove human error and restructure the workforce to focus on more pressing tasks.

JPMorgan Chase & Co launched COIN or Contract Intelligence, which automated the processing of legal documents, extraction of data, and review of certain types of legal contracts. Machine learning algorithms could use image recognition to identify patterns in the agreements. What normally would take roughly 360,000 labor hours per year, took the model a few hours.

Another example of automating tasks is the increased use of chatbots that provide quick and reliable answers to consumers. Using AI-powered mobile and web chatbots, banks can speed up the time it takes for consumers to receive answers and decrease the need for human assistants to answer questions.

Meeting consumer demand with AI and ML

One of the driving factors of AI and ML adoption is that consumers are demanding this technology from their banks. Having a secure, personalized approach is becoming the new normal for members, and since banks must rely on consumer loyalty, they must deliver on a new way to bank. Legacy systems, cost, and the skills gap will all hinder AI adoption in the financial world, but many financial institutions are overcoming these obstacles to provide a more technology-focused offering to their clients. This technology already offers a competitive edge to early adopters and will likely continue to do so moving forward.