How Banks Utilize Data Analytics. 8 Areas of Application

The banking industry is a specific one with the fact it generates and collects an impressive amount of data. Combined with predictive analytics and connectivity, data opens the door for endless opportunities of boosting business efficiency. In this blog post, we are pointing at just 8 of them. Feel free to add some more in the comments below.

Products and services personalization

Data can be used for personalized products and services creation. For instance, if you buy a car, your bank will be aware of your new purchase. This can trigger the CRM system to send you personal offer on car insurance. Or if you receive a huge bill while your bank account balance shows you do not have enough money to cover it. Then the bank can send a special offer. An opposite scenario is also a viable option. Investment products like mutual funds can be offered to customers with significant cash deposits, and so on.

Credit risk evaluation

Lending loans have its risks. Banks are heavily relying on credit risk scoring, which is a product of predictive analytics. It rates the probability a customer of paying back its credit by examination a compilation of existing data from different sources. A recent McKinsey report shares the case of a bank operating in the developing markets where the existing data was thin. Then the bank bought data from a local telco company since the phone bills payment habits are a great predictive indicator for reliable lenders.

Forecasting ATM cash out

Operating chain of ATM machines can be a tricky part. Data analytics can solve this question really easy by predicting withdrawals in terms of numbers and amounts. This way, the chain of cash machines will be filled with the right amount of money when they have to, not before or after. Meticulous planning is crucial here because the logistics of cash is not an easy task.

Processes automation

By training machine-learning algorithms, an organization can achieve processes automation. Digital assistants can effectively manage routine inquiries and provide custom advice. Because lets be honest, nobody reads the FAQ sections or the text with small letters. On top of this, machine-learning algorithms are capable of self-education and require minimal oversight. Sounds like the future of customer`s support, isn’t it?

Fraud prevention

Modern day data analytics provides the tools utilized by banks to recognize, hence act on suspicious behavior. It is possible because any customer with deposit, credit card and/or personal loan accounts have usage patterns that analytics can combine and check against fraudulent behavior in real time. Pattern analysis of average balances, transactions, purchases, etc. attributes can help banks to detect potential fraud. A bank can also protect against internal threats by using data and algorithms to monitor employees’ on-the-job activities.

banks data analytics

Customers` retention and acquisition

The Singapore branch of Citibank offered its customers specific discounts at retailers and restaurant based on the customer transactional patterns. This way, the bank significantly boosted its customer base loyalty and customer satisfaction improvement, hence the retention. It is not about just the loyalty programs. The McKinsey consulting group is stating in a recent report that undisclosed European bank turned machine-learning algorithms predicting which currently active customers are likely to reduce their business with the bank. This new understanding gave foundations of targeted campaign that reduced churn by 15%.

Informed strategic decisions

JP Morgan analyzed 12.4 billion card transaction to identify a dramatic slowdown in the growth of everyday consumer spending. This happened in 2015 and such data-driven insight helped managers to shape the bank future strategy and offerings. Fifth Third Bank relied on analytics to create a product-pricing engine, which was used for the acquisition of new customers. Applying data analytics, HSBC identified the barriers preventing its customer base to use its online banking tools. This helped the bank to create a strategy to boost the conversion rates. It is widely known that predictive analytics is far more advanced than the old school statistic modeling because of its ability to take a lot of variables in account. This is helping strategic decision making to be informed by eliminating the guessing factor.

Marketing campaign efficiency

We have already discussed the importance of data analytics in a multichannel marketing campaign. Since the banking business is among the big advertising spenders there is no surprise it is utilizing the insights hidden within big data in terms of marketing efficiency. Bank Polski, for instance, is relying on a multi-channel campaign management platform reporting a clearer picture of its marketing effectiveness in order to achieve higher ROI. Laurentian Bank of Canada is applying data analytics to identify the defining factors of a marketing campaign success across its various channels. This way, the Laurentian` marketers were capable of designing their campaigns to perform better as well as to alter if the actual run is not successful as needed.

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