Total fraud worldwide exceeds $6.3 billion a year and costs the average firm 5 percent of their revenue1
Banks need to embrace Big Data to gather 360-degree insights and gain a competitive advantage
By 2020, firms that use Big Data analytics will see cumulative productivity gains of $430 billion2
Money laundering is without doubt one of the biggest problems facing financial institutions today. Conservative estimates suggest that $1.5 trillion worth of illegal funds is moved on an annual basis, representing two percent of global GDP. While governments, regulators and financial organisations have attempted to put stringent anti-money laundering (AML) rules in place to combat the issue, these have not had the desired impact. For example, in the US, AML regulations help to seize just 0.2 percent of money that is laundered in the country.
There are many variables that have combined to put financial firms on the back foot. AML rules can cost billions to implement and often prove to be largely ineffective. Many organisations still rely on outdated fraud detection systems that use rule-based criteria, so zero-day activities cannot be picked up. Additionally, inflexible, legacy infrastructures have created an enemy within many institutions that take the form of data silos. These disconnected pools of data have made it almost impossible to collate data from different sources and analyze them accurately. But all this is changing as Big Data analytics tools are providing financial institutions with the power to fight back…
"Within a couple of days, we identified 40 fraud rings that the bank had been completely blind to until we ran the analytics"
Fighting back with Big Data and Analytics
At Sibos 2016*, Andy Schmidt, an advisor at the CEB Tower Group* consultancy, said that data is "the most under-leveraged asset banks have available to them." The good news is that Big Data platforms powered by Intel-based open and scalable architecture are now enabling financial institutions to analyze streams of data and find actionable insights. From a regulation and compliance perspective, the ability to pool together data from all sources removes the problems associated with silos such as data movement and duplication. It also means that huge volumes of structured and unstructured data can be analyzed accurately and faster than ever (in real time vs weeks). This means anomalies and risks can be identified and acted on, saving banks critical time and money.
The benefits of implementing Big Data platforms in a financial environment can be immediate, according to Chris Swecker, a 25-year veteran of the FBI and Former Head of Security at Bank of America. “In a pilot for a large bank, we pooled data from dozens of databases, then had SAS do link analysis to look for fraud rings. Within a couple of days, we identified 40 fraud rings that the bank had been completely blind to until we ran the analytics. If we would had this kind of firepower when I was working the streets at the Bureau, my hair would not be as grey as it is now,” he explained in a SAS blog.
The key to leveraging the power of Big Data is to move from the inflexible systems of yesteryear and establish robust, scalable data architecture. Investment is also needed to recruit professionals such as data scientists who have the skills and experience to input the right queries, observe trends and make recommendations.
Compliance to cash
While risk and compliance will continue to be the primary area of investment for banks, deploying the correct infrastructure will allow banks to leverage further to drive more cash/value to the business.
To achieve this goal, banks need to improve their Know Your Customer (KYC) rules and internal controls by using the comprehensive 360-degree customer views provided by analytics. It's now possible to monitor locations, preferred channels of communication, demographics, length of interactions, social media usage and more because of all the digital touchpoints that customers are using. This provides an unprecedented opportunity for organisations to learn about individual user behaviour and develop personalised products and services. Going forward, it will be the banks that offer the best-in-class experiences that will be most successful in attracting new users and retaining existing customers.
As the world becomes ever more interconnected, data will become the lifeblood of business. By 2020, it's estimated that over 35ZB of data will be generated worldwide/per year – a 4300 percent increase from 2010. Banks will need to ensure they are in a position to separate useful data from the swamp and use it to make data-driven decisions.
There is a huge opportunity in the financial sector and businesses with a real sense of urgency will be the winners. IDC predicts that by the end of the decade, firms that use data analysis and execute on actionable insights will achieve an extra $430 billion in productivity benefits over competition that neglects analytics. This is a huge slice of income that all businesses including banks cannot afford to miss.
Together with its ecosystem partners, Intel is committed to bringing high-performance and efficient solutions to market, and help make sure that your financial institution has the tools to give it a competitive advantage.
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