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Data Management in Banking: How Do You Unlock Value?

by Graham Goble on September 23, 2021

Topics: Banking Business Intelligence

Financial institutions have access to more data than ever before. For CFOs, the challenge is finding a scalable, cost-effective, and repeatable way to turn this raw information into actionable insights.

Data is often described as the new oil. Although it’s not particularly valuable in its raw state, it has immense potential in the form of actionable insights and powerful business intelligence.

Unlocking that hidden potential is easier said than done, especially in the financial sector.

CFOs face many technological and operational challenges. But if you can overcome them, there are considerable benefits to a smarter, more agile approach to data management in banking.

The Challenges of Data Management in Banking

1. Inefficient Manual Processes

Larger banks and credit unions build bespoke data warehouses to manage their data. They employ dedicated, in-house teams of analysts to organise and interrogate every megabyte, turning raw information into actionable business insights.

It’s a costly and resource-intensive process that simply isn’t an option for smaller, regional institutions. They must make do with static, manual platforms like Excel, and this presents its own challenges.

It takes time to build an Excel model from scratch. And in our experience, the only one who knows how to use it properly is the person who built it. What happens if they’re on holiday or off sick? Relying on a single, key individual to process your data only increases the risk of errors creeping in.

Creating financial reports in Excel takes even longer. Depending on the amount of data, it can take weeks to create a single report.

Manual approaches are also prone to human error. Regulators can issue huge fines for inconsistencies in your financial reports, so it’s important that your data is as accurate and up to date as possible.

2. Consolidating Data from Multiple Sources

When you have data from multiple sources in multiple locations, it can be difficult to navigate and make sense of the chaos.

If you want to create a balance sheet or income statement, for example, you must be able to pull data from both your general ledger and core banking system. You also need to reconcile the data. Otherwise, any reports or insights you create will be inconsistent at best, inaccurate at worst.

This presents a serious problem for smaller institutions. Most lack the tools and analytical expertise to consolidate and extract insights from their data.

Without this capability, it’s impossible to establish a single version of the truth – a vital prerequisite to maintaining consistency across your institution and avoiding discrepancies between data sets.

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3. Low Visibility

Financial reporting is a long and onerous process. It takes time and effort to source, compile, and process the raw data you need to build them – and that’s without considering the additional hassle of checking your work for discrepancies.

Things become more complicated the deeper you dig. The more detail required, the more sources you need to complete the picture and the further you go beyond the limits of your general ledger and core banking systems.

General ledgers are only designed to give you a top-level view of your financial statements.

They don’t provide the granularity or visibility you need in most reporting scenarios. For instance, you can’t track the number of new loans over the past month from your general ledger, let alone volume by loan officer or branch.

But financial institutions increasingly need this level of insight to drive performance and operational efficiency.

Unlocking Value Through Automation

There’s a common misconception among financial institutions that a data warehouse provides the visibility, consistency, and analytical capabilities you need to establish a single version of the truth.

A data warehouse can certainly make it easier to access and organise your data. But you still need to apply a layer of business logic on top of it to unite your data sources and turn those megabytes into insights.

Trying to do that manually is a bit like attempting a 500-piece puzzle without the picture to guide you. You’ll get there eventually, but it will take three times as long.

A Smarter Approach

There are two ways to approach data management in banking:

  1. Build a custom, in-house solution
  2. Invest in an automated, cloud-based application that has these rules built into its DNA

Banks are increasingly choosing option number two. It’s not only a more cost-effective and agile solution, it addresses all the major challenges facing today’s financial institutions.

Automation enables you to quickly consolidate and reconcile disparate sources of data. A reconciled set of financial data allows you to move into the realm of analytics, helping teams across your institution unlock a range of actionable insights that inform banking performance.

This level of insight goes beyond improving process efficiency and can have a direct impact on your bottom line.

It allows you to answer vital questions, like “Which customers have a deposit that’s about to mature?” Or “Who can I pre-approve for a loan?” By proactively engaging with customers, you can ensure those deposits don’t leave the bank.

One of the most common applications is reporting. Central banks and regulators across the globe are encouraging financial institutions to automate their financial reporting to streamline the process and reduce errors.

The Benefits Extend Beyond Finance

It’s not only finance teams that benefit from better data management in banking.

The ability to harness your data and transform it into actionable business insights can support a range of functions – from risk management to sales and marketing.

CFOs are perfectly placed to drive this change. With overall responsibility for financial data in their institutions, they know what they need from an analytics solution and can engage with stakeholders to communicate these benefits.

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