Retail Banking Fraud - Market Overview

  • Retail banking fraud encompasses Application level fraud and transactional level fraud 
  • Application fraud comprises fraud committed at the application stage across account opening, and secured and unsecured credit product applications whereas transactional fraud refers to that occurring during transactions such as account takeover and payments 
  • While technologies in POS like EMV (chip, tokenization and encryption) have decreased transactional fraud losses, application fraud is still a major issue in the banking space

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Global Financial Fraud Trends

  • New-account fraud rose 70 percent in 2017,with account takrover fraud in the US growing three times over 2016,to reach a total loss of $55.1 billion 
  • Identity theft is a major component of new-account fraud where good credentials are used as facilitators for committing application fraud E.g., Social security number breach accounted for 500,000 fraud victims in 2014 and is expected to rise to 710,000 by 2018 
  • Identify Fraud number/victims reached 16.7 million in the US in 2017,an increase of 8 percentage over the previous year.ID fraud constitutes to 6.64 percent of consumers
  • Total value netted by fraudsters was estimated at $16.8 billion in the US

  • With regard to online fraud, CNP fraud accounted for 81 percent against POS fraud

Retail Banking Fraud Prevention Systems - Global Market Maturity

  • While there are many players offering analytics based financial fraud detection software, there are only few players catering to the retail banking space. This is a direct consequence of regional/national regulations. Hence, the maturity is closely tied with the banking landscape
  • While emerging economies still lag developed countries in terms of fraud technology adoption, they have the opportunity of leapfrogging into adopting advanced fraud detection software across the enterprise. (Its peers in the developed world face challenges integrating its legacy systems)

Supply Trends and Insights

Global/Regional Supplier

  • Reactive to predictive analytics: Advanced analytics techniques based on machine learning such as neural networks and self organizing maps are being used to identify fraud patterns and vulnerable customer applications. This in turn provide fraud scores for customer applications indicating proximity to a benchmark fraud characteristic 
  • Big data: With the explosion in volume, variety and velocity of data, fraud prevention systems are gearing towards capabilities that enable digestion and monitoring of customer information across his/her entire lifecycle. E.g., looking at buying patterns to discern any unlikely/deviant purchase behavior in comparison with historic purchase behavior. These services are mostly provided as a bespoke managed service post license purchase 
  • Device intelligence to digital footprint: Apart from device intelligence services encompassing channels such as mobile, e-mail, IP, etc., there is also a need to look at synthetic identities where there are sudden spikes in credit requests from a seemingly authenticated source

Vertical/Local Supplier

  • Vertical focused solutions:While there are few suppliers focussed on the retail banking vertical due to the need for dynamic regulatory systems, there are information sharing agreements through databases at the national level that contain cross-member (financial institutions and insurance) application data that help in screening for fraud at the application stage E.g., CIFAS, Hunter, SIRA in the UK, IFI in the Netherlands,FPN in Germany
  • These fraud databases are also sometimes combined with data from other industries such as telecom data E.g., Fraud prevention pool in Germany

Engagement Trends

  • Most adopted engagement model globally: Single vendor with a bespoke service
  • Licensing metrics: The most widely used metric is volume of applications/number of submissions into the system. Some suppliers also use ‘pay per click’ where in the initial submission and any updates or re-checks are counted separately
  • Additional services such as integration to external consortium data through adaptors or APIs are charged as a separate fee. E.g., FICO Falcon Fraud Manager integrating with Hunter database