How Predictive Analytics in Banking Gained Reshaping Financial World?

Rate this [type]

Through the years, data analytics has been an instrumental part of the financial world. From credit scoring to investing, data analytics has helped investors at every step of the way and the next big thing is predictive analytics in the banking industry.

With the advent and adoption of advanced technologies, big data analytics gained tremendous importance. The use of big data analytics in financial institutions opened up new potential and unlocked new opportunities, boosting the market growth. As per research, the global industry of predictive analytics in banking worth $5.43 billion by 2026, growing at a CAGR of 20.8% from 2019 to 2026. The growing adoption of the internet of things (IoT) and significant increase in fraudulent activities such as payment card fraud, accounting fraud, and money laundering are the prime drivers for this growth.

How predictive analysis gained importance in banking?

Predictive analysis has the potential to improve customer experience in a lot of ways. However, it could be overwhelming to know that banks have so much information and they rely on algorithms to make vital financial decisions that directly affect customer’s life. On the contrary, computers are unbiased and always disposable. The widely used example of predictive analytics is credit scoring models to know your creditworthiness.

For instance, the FICO credit score is used to foresee your behavior and know how likely you can miss payments, which is based on how borrowers with a similar background like you have performed in the past. Along with this, computers can help manage finances and identify when your income and expenses affect your bottom line. This could help prevent several financial problems. For instance, your mortgage payment is due on the 10th day of every month and you are running low on cash, your bank can send you an alert.

However, the reason why predictive analytics in banking gained so much attention is fraud prevention. Even though you take good care to secure your identity, sometimes it is out of your control. The incidences of data breaches are becoming more and more common and thieves can your card number or other confidential information to steal. Predictive analytics is the best way to spot such problems. Banks with predictive analytics could know instantly if someone logs in your account without your authorization. Moreover, it can reduce bad check scams by analyzing data patterns.

Predictive analytics in banking is beneficial for financial management and loan approval. Not every applicant possesses a high FICO score. However, that does not mean they do not qualify for loans. Many have never established credit and some are faithful borrowers even having negative credit reports. A detailed analytical study can help lenders evaluate criteria for loan approval and help non-traditional borrowers with financial aid.

As predictive analysis is nothing but studying patterns, it can help people see the bigger picture using making sound financial decisions. For instance, the predictive analysis could review your overall income and expenses and find out whether you should make extra payments on the loan or help you save more to reach your financial goal. Predictive analytics in banks could extend help toward their customers by offering them personalized service to gain higher rates on their savings. This could help build a faithful consumer base.

Even though cutting-edge technologies such as big data and artificial intelligence have just stepped into the financial world, to some degree, financial institutes have already adopted advanced technologies. It could be tiresome for customers to deal with an extra layer of security and prove that they are not thieves while accessing their bank accounts. However, if it prevents data theft, both banks and customers are not willing to place their bets on predictive analytics for the greater good.