In this age where banks need to compete for customers, one of the banking industry’s key focus is to rebuild trust with its customers. One way is to understand the client more deeply to improve services and offerings. Firms are now looking at new big data technologies including Hadoop MapReduce to help analyze vaster quantities and types of data for value-add services.
One emerging use case for big data is in the area of Financial Crimes. With the number of electronic and mobile payment estimated to reach almost 50 billion transactions in 2013, electronic fraud represents the area of strongest growth. The United Nations Office on Drugs and Crime (UNODC) estimated that $1.6 trillion or 2.7% of global GDP were laundered in 2009. On a regional basis, the numbers are even more staggering. French card issuers estimated fraud at over 450 million Euros in 2012.
Detecting organized fraud is a challenge for most banks. For example, in banking, one or more perpetrators will open multiple accounts with less than $10,000 in each to avoid detection. Identifying potential money laundering activity means discovering hidden relationships in data from multiple sources. The sources can include customer call logs, PDF files, email, images, etc… Traditional transaction monitoring systems were not developed to handle this activity. Much like data warehouses weren’t design to handle the petabytes of big data generated hourly nowadays.
Big data technologies such as Hadoop MapReduce allow firms to more deeply understand activities that impact their firms. Using IBM InfoSphere BigInsights, GPFS and Platform Symphony to augment traditional data warehouse systems allow firms to analyze and store significantly more data cost effectively. One IBM client is using IBM Platform Symphony grid middleware to accelerate its MapReduce analysis of mortgage loan applications. The applications – in the format of Adobe PDF - include both text and images.
By adopting a big data focus, firms not only can analyze greater volumes of data but also greater data varieties in a more cost and time effective approach, for both revenue and risk management. In the area of Financial Crimes, big data technologies can help firms uncover potential illegal activities faster and with more confidence. Organized crime demands organized actions and collaboration among banks, retailers, consumers, and law enforcement agencies. By sharing best practices, banks may be able to stop or deter fraud on a larger scale and go a long way to rebuilding consumer trust.
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