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Big Banks Turn to Analytics to Improve Risk Management, Predict Customer Behavior

Big Banks Turn to Analytics to Improve Risk Management, Predict Customer Behavior

The data analytics revolution in the business world has also reached major banks. Of all the industries tapping into the power of analytics, few have seen immediate advantages like the banking industry.

In a business governed by numbers, banking leaders have turned to data analysis in a number of key areas. They include predicting customer behavior, risk assessment and driving revenue.

The movement has created cost-savings for banks. It also has created yet another job market for those with degrees in data science and business analytics.

For those banks committing to analytics with time and money, analytics “can and should become a true business discipline” within banking, according to a 2017 report from McKinsey and Company.

How Banks Benefit From Analytics

 Banking turns to data science in a number of areas. Tools for business analytics have reached a level of sophistication that allows for extracting actionable conclusions from large data sets.

Some of these areas include the following.

Risk assessment. Banking is built on risk. Millions of data points now allow for better assessment of the risks involved in making loans and investing cash. Business analytics gives banks insights into millions of transaction as well as branch-specific information. For example, loans for a building renovation can be assessed on age of structure and geographic climate conditions as well as data on the loan applicant.

Marketing and Sales. Data gathered on customers helps banks better market services based on the customer’s past behavior. This helps banks predict customers’ future needs. This targeted marketing and sales effort means customers are offered services they need rather than a “one size fits all” approach that barrages customers with every possible service.

Performance. Analytics can track performance of bank offices and employees in real time. This helps determine whether goals are met and also helps determine peak and off-peak work times, helping banks to better schedule training for employees, for example.

Regulatory compliance. Software systems also can track various government regulations involved with banking, which change fairly frequently. Data is collected, organized and analyzed to ensure regulatory compliance.

Cutting Costs, Increasing Profits. As with every industry that uses analytics, proper collection and interpretation of data on day-to-day operations can lead to reducing inefficiencies and increasing profit. McKinsey’s report mentions one bank that increased profits 8 percent by analyzing where discounts were offered and eliminating the ones that were unnecessary. Another used analytics on customer behavior to increase customer retention by 15 percent.

Jobs in Data For Banking

All of these areas require skilled workers in data science and business analytics.

The competition among banks for hired workers skilled in these area is “heating up,” according to Computer World. Two main areas dominant the hiring: Big Data engineers and data scientists.

The engineering group works more in designing data collection systems. The second groups come from a data science and business analytics background. They are charged with helping business leaders use conclusions drawn from data to make important decisions.

This requires not only skills in data collection and analysis but also communication skills. Data scientists and business analysts need the ability to explain complex findings in a way that makes the data understandable for executives.

Data scientists are in the highest demand. It’s also where the largest skill shortage is present, according to Computer World.

Clearly, a degree in data science and business analytics can open the door to a stable and rapidly growing field. And one of the main areas where demand is expected to continue is in banking.

2017-08-25T14:57:43+00:00
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