3 Banking Technology Trends to Watch in 2019

The banking and tech sectors have traditionally shared a longstanding and mutually beneficial relationship, with banks investing heavily in new technological innovations, and new tech subsequently reducing banking’s operational costs. Banks have often showcased the latest technology to the public, with once groundbreaking technology such as mobile and online banking now largely taken for granted.

However, the benefits of technological innovation in the financial sector extend to more than just ease-of-access for banking customers, and banking’s ‘backend’ has likewise seen tremendous development from recent technological innovations.

With over 88% of digital commerce users conducting personal banking online in 2018, strong  technological innovation in fraud detection, credit agreements, big data analysis and predictive analytics are more important to banks than ever before.

Research and development budgets in banking are predicted to grow globally to over $270 billion during 2019, and here we explore the three latest tech innovations they’re banking on:

1. Automatic Fraud Detection

Although fraudulent activity in banking has fell in recent years, four out of five customers still cite fraud protection measures as a priority when choosing banking providers. So, to retain customers, it’s little surprise that banks are investing heavily in automatic fraud detection software.

Traditionally, banks automatically lock or limit accounts associated with suspicious activity, such as new and exotic login locations or erratic spending. But these measures are often too little too late, or at great inconvenience to their customers.

Auto detection methods don’t act retrospectively when it comes to fraud–they stop it in its tracks before it even takes place.

Technology solutions such as ‘IntSights for Financial Services’ are designed to help banks anticipate fraud attacks before they happen, by crawling and monitoring millions of clear, deep and dark web sources, and collating attack indicator information for the bank.

Big Data

2. Big Data and Predictive Analytics

More customers using online banking means more data, and more data requires greater levels of analysis. Thankfully, we’re long past the days when banks had to deal with such voluminous data manually.

But there are still big issues in big data processing, and most prominent among these is inferring meaningful information from databases to inform business decisions. Predictive analytics, based on predictional sciences, holds untold potential for banking services. Predictive analytics offers banks the ability to understand and predict customer behavior, often before the customer knows what they want.

A bank’s marketing department wishing to identify potential customers for a new loan, would typically use machine learning models to identify potential customers based on specific criteria–such as credit scores or income.

But with new predictive analytical technology, banks will be able to detect customers who need a loan using an analysis of a customer’s short-term life events, for example recent graduates, newly-wed couples, or those who have recently divorced–in other words, life events which precipitate a customer’s needs.

As significant as machine and deep learning models have become to big data techniques, the raw data means little without the aid of such signals to provide context. Currently, as it takes some serious academic people power to perform and interpret, predictive analytics is the realm of ‘unicorn’ data scientists, who are often difficult and expensive to find and hire.

Data science companies are now working towards shifting this paradigm—Endor, for instance, has built an AI-powered predictions platform, following MIT’s science of “Social Physics”–a new field of study focused on predicting mass human behavior. Using these field-tested models, Endor allows banks to ask pinpointed questions, aimed at predicting user behavior, such as “Which type of client is most likely to take a loan?”.

Banks that adopt and implement emergent AI-powered predictive analytics platforms will surely be at an advantage compared to their competitors, expanding customer reach while minimizing marketing spend.

3. Machine Learning for Credit Agreements

Credit agreements are rarely easy for banks or customers. With a huge amount of customer personal data involved, and the need to make sound business decisions, application for credit can be a lengthy process.

Identifying credit-worthy borrowers is an immense task for banks. Despite default rates on credit cards improving, customers defaulting on their loans and credit is still problematic for lenders.

But banks are reducing their risk and exposure to bad borrowers, while keeping credit approvals growing, through machine learning technology.

Machine learning operates on top of legacy credit scoring techniques, using sophisticated math and a greater depth of data to identify statistically safer borrowers.

Firms such as ZestFinance are implementing their proprietary ZAML machine learning technology, based on game theory, to improve consistency, accuracy and performance of credit approval suggestions.

This means that banks will be able to make better informed loan decisions, even for applicants who have very little visible credit history.

Outlook for 2019

With the continued development of such innovative technology, 2019 could see huge reductions in fraud, credit rejections and defaults, and a marked increase in bank marketing campaigns utilizing the latest and greatest in big data analytical techniques.

More importantly, despite so many disruptive technologies entering the financial sector throughout 2018, it seems banks are still investing heavily in staying technologically relevant–a needed boon for promising tech startups.