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Big Data and Analytics in Banking: Three Trends to Know

In a recent IDC survey, U.S. banks named big data and analytics their top priority among investment initiatives for the year. There are several reasons for the upsurge of initiatives focused on big data and analytics in banking. Let’s explore these trends and what banks hope to gain from their big data investments.

1. An Upswing in Fraud and Other Security Risks

There are many fronts in the war on fraud. Bank fraud can be as low-tech as a thief stealing a checkbook and writing bad checks that wipe out a customer’s bank balance. It can be as high-tech as a hacking ring that uses automation tools to manufacture hundreds of fake identities that apply for credit that is abused and then defaulted on. Identity theft. Skimming devices at ATMs. Ransomware. Mortgage and securities fraud. Criminals continually devise creative ways to steal, which makes tracking humanly impossible.

As a result, finance sector security teams are relying on analytics in banking to address these issues. The digitization of banking has allowed institutions to gather valuable data about their customers. That includes what times of day customers typically access online accounts, devices used to gain access, and a general range of transaction types. The majority of transaction types may fall within a specific value range and occur at common places, like a particular grocery store, gas station, or movie theater. With machine learning informed by all this transaction data, systems can learn when a transaction falls outside the norm and alert the customer that something may be wrong. For fraudulent credit applications, bank systems may know that a typical customer takes 10 minutes to fill out a credit application. If someone logs in and fills out an application in two minutes, and if such short application sessions are occurring in large numbers, that may be the first sign of a credit card fraud ring.

2. A Need to Simplify and Speed Up Compliance

FATCA, CRS, KYC, AML, SAR, OBS, Basel III: Banks and other financial institutions face an alphabet soup of regulations that can vary from country to country. Organizations must follow the law, but like tracking fraud, complying manually can be a challenge, and failing to comply can result in crippling fines. Many organizations have been forced to add staff to cope with the workload, but even with extra staff, they often fall short. Relying on big data in banking is necessary to simplify the compliance process.

Many regulations focus on money laundering and other suspicious criminal acts. These activities depend on hiding within the tidal wave of transaction data that flows daily into a bank. To respond, many banks see the value in transitioning from human-led compliance, which often consists of periodic activity checks, to technology-led compliance, which allows real-time, continuous monitoring. That transition requires a big data architecture that can access both historical records and real-time transactional data. Modern data architectures allow compliance teams to process information in real time to uncover risks before they create harm. Big data analytics paired with machine learning can identify patterns much better and faster than humans. The goal is to improve how client and institutional risk is managed. This infrastructure also allows organizations to quickly respond to the ever-changing regulatory landscape, ensuring that banks always follow the rules and laws they’re subject to.

3. Customer Demands for Greater Responsiveness and Personalization

Customers want the businesses they patronize to know them and anticipate their needs. That customer sentiment is as true for banks as it is for clothing and grocery stores. Banks are uniquely positioned to gain insight into their customers’ wants and needs through access to transaction history, spending habits, loan data, and creditworthiness. Savvy financial organizations look to big data for smarter ways to approach their customers, and to determine which offers will resonate and drive customer loyalty.

Using a big data architecture, financial organizations can form a single view of the customer. This single view can reduce the friction that customers encounter when interacting with their banks. The experience should be seamless, whether the customer is inside a brick-and-mortar location or accessing the bank’s mobile app on a smartphone. This is how customer loyalty is built. With this single view, a bank can better target and retain customers, and deliver insights and offers that resonate with their clientele.

As these three trends gain in importance, is your financial institution prepared to respond? Big data in banking demands an enterprise-wide strategy, so make sure you’re ready and thinking ahead.

To learn more about how banks are using big data, read this IDC report.


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