As banks begin to serve increasingly complex needs, data and analytics in banking become ever more important, helping to understand clients and increase sales.
Clients want more personalized services, and expect banks to have industry-specific expertise and capabilities to help them meet their challenges, including decarbonization and net-zero financing. (Banks can no longer afford to ignore ESG issues.)
Adding to banks’ list of headaches are fintechs, which compete for market share in areas such as securities trading, payments and lending.
It is no wonder that data and analytics in banking have never been more relevant. Luckily, with the general shift towards digital banking (78% of adults in 2022 in the US preferred to bank via website or mobile app, according to Forbes Advisor), collecting data has become less of a hassle.
Introduction to Data and Analytics in Banking
Traditionally, banks and financial institutions were product centric.
Nowadays, with the evolving tech landscape, they have become customer centric. Analytics in banking can help banks stay ahead of the competition and refine internal operations.
Data analysis tools help banks to make informed decisions with strategic insights. These in turn personalize customer experience, increase revenue, and result in better outcomes. Generally, data analytics in banks is used for supply, demand, and risk management.
How Banks Use Data & Analytics
Here are just some of the uses of data and analytics in banking.
1. Credit Risk Analysis
Credit risk analysis involves analyzing historical data to understand creditworthiness or evaluate the risk of granting a loan.
Analytics can be used to manage risks associated with loans. Banks do this by monitoring data collected on customers. Such data may include:
Total debt (amounts owed on different credit cards)
How much is owed (credit card use)
Customer credit score
2. Fraud Detection
Instead of only detecting fraud, analytics can be used to manage risk.
They can identify and rate customers who are at risk of fraud. Hence, banks can apply various levels of verification and monitoring to their accounts. This further allows banks to prioritize efficiently in fraud detection.
If you are concerned about fraud, you should consider audit and anti-fraud assurance services.
3. Risk Modeling
Risk modeling involves simulating how an asset or portfolio of assets (such as bonds, options, stocks, etc) moves in response to various scenarios. When done correctly, banks can reduce a portfolio’s risk.
For instance, if a bank wants to conduct an investment banking transaction, they would have to consider:
Risks and their probability
Importance of transaction compared to alternatives
4. Branch and Online Channel Sales Analytics
When it comes to branches and online sales channel sales, banks need to consider what is coming through and going out to the channel, and how much cash they have.
Generally, branch sales account for a smaller percent of total sales, but are more profitable per capita. On the other hand, online transactions do not generate as much profit per sale, but account for a larger percent of total sales.
The key, in the majority of cases, is to see the bigger picture and use data analytics accordingly.
5. CLV Prediction
Customer lifetime value (CLV) measures how much money a customer is likely to spend in their lifetime. Comparatively, brand value measures the amount a customer is willing to pay for a service or product.
For full optimization of business models, banks need to consider both. They should be wary of falling into the trap of traditional analytics, which favors the former, thus impacting revenue.
With data and analytics tools, banks are better able to meet modern customers’ demands. With them, banks can focus on adding value and prioritizing opportunities.
Efficiently implementing data and analytics in banking is difficult, as banks need to tackle agile delivery, data, technology, strategy, operating models, and talent. However, being able to do this can lead to a powerful impact and enable banks to scale up.
All this takes time, resources and money. To ease the load, NextGen Accounting offers credit card reconciliation services, bank reconciliation services, reconciliation automation, financial consulting, financial reporting, and audit and anti-fraud assurance. Our services are specially tailored to firms with vast amounts of data that are extremely difficult to reconcile.
To give you the fastest and most accurate reconciliation, we use our patented software CrushErrors, which you can also obtain as a product if you’d rather conduct reconciliations in-house.
NextGen Accounting’s management team has decades of experience and includes former executives of Barclays Bank, Bank of America, and ICBC. Contact us today for reconciliation services or book a free demo if you’d like to get CrushErrors!