Fighting fraud in banking with big data and analytics

Global Banking Industry Marketing, Big Data, IBM

Converging forces are creating the perfect storm for a heightened focus on fraud and financial crimes, which are more sophisticated than ever before and are increasingly part of an organized criminal enterprise. Organizations are paying a high price when financial crimes are successful, and the reputational costs can be greater than the initial financial impact.

Fraud and banking

Banks are moving to new paradigms and technologies (such as cloud and mobile), which add new complexity in facing these growing challenges:

  • robert palmer banking counter fraud blog post 09214.jpgDirect financial losses stemming from fraud and financial crimes  
  • Indirect losses from reputational impact
  • Significant operational costs to address the problem
  • Customer satisfaction issues through inconvenience caused by false positives hampering legitimate transactions

All of this has increased the need for bank executives to improve their effectiveness in fighting fraud. But today’s organizations aren’t as well equipped to fight fraud as they could be. Current systems often cannot handle the volume, frequency or the complexity of today’s fraud activity. Also many banks have, over time, cobbled together numerous fraud point solutions, making a holistic view of threats impossible. 

The old model of responding to attacks and fraud well after the fact just won’t work in today’s world of sophisticated and organized financial crimes. Adding to the problem, banks have created a corporate silo mentality that’s often a barrier to successfully fighting fraud. As a result, too many organizations remain vulnerable to fraud because they aren’t taking advantage of new capabilities to fight these threats.

Four phases of enterprise counter fraud

An integrated approach is needed not only to respond to fraud and financial crime, but to proactively anticipate, detect and mitigate threats. This integrated approach should address these key phases of enterprise counter fraud measures:

  1. Detect: Apply advanced analytics to all key fraud data to predict whether an action is potentially fraudulent before losses occur. Looking at small sets of data reduces a bank’s ability to prevent or detect sophisticated crime. The more volume and types of data an organization can analyze (and with greater velocity) the better their success will be against internal and external threats.
  2. Respond: Apply fraud insights to take action in real time. Use analytics on streaming data to confidently differentiate legitimate actions, while preventing or interrupting suspicious actions and respond immediately to criminal patterns and activities.
  3. Investigate: Turn fraud intelligence into action. Perform and manage inquiries into suspicious activity that are supported by thorough data analysis and collaborative, sophisticated case management.
  4. Discover: Use new big data and analytics capabilities to identify suspicious activity by analyzing mountains of historical data to search for patterns of fraud and financial crimes.

Counter fraud best practices

To assist an organization in their journey to improve fraud and financial crimes outcomes, there are best practices that have shown to be successful:  

  1. Elevate the agenda. Elevate fraud to board level responsibility and establish cross-organizational leadership to counter a silo mentality.
  2. Act with insight superiority. Expand the fraud observation space, apply deep and layered analytics at each level of the fraud lifecycle and act with speed on suspicious activity as it is identified.
  3. Adapt with agility. Leverage technology to automate response, implement a lifecycle management approach to continuously adapt to evolving threats and engage with industry wide, global intelligence networks.

To join the discussion on fighting fraud with big data and analytics, visit the IBM booth at SIBOS in Boston September 29 through October 02.

Learn more about fighting fraud and financial crimes with big data and analytics today