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How Big Data in Finance Is Increasing Security

작성자:
Ronda Swaney

Data is the engine of the finance industry, and always has been. Perhaps that’s why digital transformation has been harder for this sector to embrace. Rife with legacy systems—older, less-advanced infrastructure, and applications—this sector struggles to adopt newer technology fast enough. That slower pace can create added vulnerability as financial companies try to fight fraud. Fraudsters never lack ingenuity, and banks and other institutions must look to big data in the finance industry to address the constantly shifting cybersecurity and fraud landscape they face.

The Necessity of Digital Transformation

In the 1960s, financial institutions were the original early adopters. The sector embraced mainframes as their workhorses for the heavy processing tasks that surrounded customer transactions, mortgages, and other loans. These large investments provided a reliable and steady foundation for decades. Continued reliance on legacy systems, however, opens up a new avenue of risk for a sector that is risk-averse.

Financial organizations that remain overly reliant on legacy systems experience hazards on a number of fronts. One is continued reliance on mainframes, when the engineers who are equipped to service these systems are retiring without new engineers trained to take their place. Start-ups that embrace new technologies offer another threat, effectively sidestepping organizations that fail to keep pace. Financial institutions that fail to embrace more open systems imperil customer loyalty by alienating a consumer base that demands greater ease and transparency from their transactions. The finance industry has seen the writing on the wall and understands that digital transformation is a necessity for future survival and success.

The Growing Role of Big Data and Analytics

An IDC survey revealed that 28 percent of banks see big data and analytics as their top priority for investment. This embrace recognizes the expanding role that big data and analytics play in this sector. The survey also revealed that the primary purpose behind investments in this area is to understand and target customers better. When customers have more options and it’s easy to move to new financial institutions, it becomes a challenge for banks to strengthen consumer loyalty. Banks can use predictive analytics to generate better, faster insight into the customer journey. For example, Freddie Mac uses big data to improve the mortgage-appraisal process, helping to shorten the time it takes to gain insight and make decisions. A process that once took three to six months now delivers the company insight in just one day.

Regulation and legal compliance present another challenge to the financial sector, especially for international organizations where laws vary from country to country. Modernized data architectures help financial institutions cope with the ocean of data contained within transaction histories. Reliance on big data and analytics helps the finance sector simplify the compliance process and avoid devastating fines.

Fraud Detection and Prevention Powered by Big Data

Fraud has always been a shadowy specter lurking along the edges of the finance industry. The increased digitization of financial transactions allows that specter to spread its influence from the traditional financial world to the online world. Fortunately, the industry has been able to exploit the advantages of big data analytics to fight that influence and strengthen cybersecurity.

  • Revealing credit card fraud through behavioral data. The digitization of banking has allowed banks and other institutions to gather valuable customer data. That data includes where and when customers typically access accounts, their typical transaction types, and even what the devices are that are used to make those transactions. With this information, financial organizations can formulate a behavioral baseline and then use machine learning to create alerts when transactions deviate from that baseline.
  • Exposing fraud when it’s an inside job. Not all fraud is executed by outside actors. As with external credit card fraud detection, big data analysis can use behavioral patterns to uncover fraud happening from within a company. These models define normal behavior tied to specific job roles and send alerts when that behavior is outside the norm.
  • Uncovering money laundering that tries to hide in plain sight. Millions of transactions occur across financial institutions every day. Money launderers try to hide within this tidal wave of data. It’s humanly impossible to manually check each transaction. Banks now continuously monitor transactions using machine learning to find anomalies much faster. Using modernized big data architecture, compliance teams uncover fraudulent activity in real time so they can stay compliant with regulations that govern the reporting of suspicious activity.

Fraudulent activity and cybercrime remain a costly nuisance to the finance sector. Big data and analytics, when properly applied, can ultimately reduce that cost and extract a greater price from con artists intent on committing these crimes.

To learn more about utilizing big data in the finance industry, download this white paper.

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