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Inter-bank Data Sharing, the New Frontier of Fraud Detection and Prevention

This blog is a guest blog from Tari Schreider, Strategic Advisor at Aite-Novarica Group.

Canadian Bank Fraud is on the Rise

Fraud is a growing epidemic that has spread to financial institutions throughout the world. Although fraud is a global problem, some countries, including Canada, experience a greater incidence of financial fraud than others. A recent research study conducted by TransUnion found that the rate of suspected digital fraud attempts against financial services companies in Canada increased 218% from the last four months of 2020 to the first four months of 2021—double the global rate.[1] To put that in perspective, the total annual revenue of Canada’s top seven banks was used as a baseline against the LexisNexis® 2019 True Cost of Fraud Study, which reports that fraud costs financial services firms 1.78% of revenue. The top seven Canadian banks had generated CA$161 billion in revenue in 2020; applying LexisNexis estimation places their annual fraud at an alarming CA$2.87 billion in 2020 —and this is just the known fraud. But is this the true cost of fraud? LexisNexis doesn’t believe so. It created a product called the LexisNexis Fraud MultiplierTM. It believes that for every CA$1 of fraud, a financial services firm incurs an additional CA$3.46,[2] suggesting the cost of fraud to the Canadian financial services industry is upward of CA$9.93 billion for Canada’s top seven banks – and this only seems to be growing.

Fraud Schemes Becoming More Sophisticated

Banks have for years made good on customer losses to protect their brand reputation, considering fraud a cost of doing business. Unfortunately, this approach has negatively influenced investment and innovation in fraud detection and prevention. Fraud is not going away, quite the opposite. Faceless banking has given way to emergent sophisticated account takeovers, synthetic identity schemes, and money mule gangs, pushing even the most sophisticated fraud detection and prevention solutions to their limits. These types of fraud all have one thing in common; they typically create abnormal customer account behaviors and device usage. But that abnormal account behavior can cross over to multiple banks. Fraudsters are (correctly) betting that banks won’t be able to track their activities as they move from bank to bank, perpetrating their fraudulent triumphs. They also count on the bank’s hesitation to add tighter security measures and verification steps that add friction to the customer experience. Canadian banks have a vested interest in sharing indicators of fraud to impede the growing epidemic of financial fraud. Banks must become equally sophisticated in detecting and preventing fraud and look for new and innovative ways to detect fraud not only within their bank but across the Canadian banking system.

Detecting Fraud While Preserving Customer Privacy

Analyzing customer bank account and device data held between competing banks to detect and prevent fraud is the new battleground on which fraud must be fought. Banks must learn to become frenemies, acknowledging the growing fraud landscape is a shared problem that requires a collaborative solution. Because Canadian banks don’t share account and device information, banks have a myopic view of mobile channel fraud. Considering that 77% of people own a tablet, laptop, or desktop computer, and 85% own a smartphone, the top seven Canadian banks would need to monitor 105.8 million devices as potential fraud vectors.[3] To accurately identify signs of an account takeover, such as a fraudster using a single device to access many accounts across multiple banks, one needs access to device information from many banks, not one. Current fraud detection and prevention programs need access to a broader customer and victim dataset. The data must be shared confidentially and securely where banks gain from the sharing and would not be negatively impacted by revealing competitive or sensitive information about their customers.

Tomorrow’s Solution, Available Today

One can’t fight today’s fraudsters with yesterday’s approaches, continually churning the same customer and device data from a single bank, hoping for a greater result in reducing bank fraud. If banks could share their indicators of fraud and other data drawn from their customer account and device information rather than relying solely on their own data, and this sharing could occur without revealing identifying customer and device attributes, any apprehensions of inter-bank data sharing would quickly disappear. Enter homomorphic encryption, where mathematical operations can be performed atop encrypted data, extracting insightful fraud analysis without seeing the underlying data. It would be hard to dismiss a fraud detection and prevention solution that allows data queries to investigate fraud with zero privacy loss. Homomorphic encryption allows banks to collaborate on identifying trends in suspicious customer behavior and profiles that could predict or flag fraud before it happens and then share the insights with the banks for mutual benefit.

Protecting the Banking Homeland

The COVID-19 pandemic has led to the fraud pandemic banks are currently suffering. The evolving fraud landscape, the shift toward open banking, and payment modernization should instill a sense of urgency among banking executives to improve fraud prevention. Increasing competition in the banking sector will squeeze profit margins, making it more difficult for banks to absorb fraud losses as a cost of doing business. Banks need to consider fraud detection, and prevention approaches beyond existing solutions when maturing their programs. Inter-bank data-sharing is the way to reach herd immunity where the Canadian banking industry becomes an arduous place for fraudsters to operate.


[1] “TransUnion Research: Digital Fraud on the Rise as Online Banking Increases,” Canadian Security, June 16, 2021, accessed October 17, 2021,

[2] “2020 True Cost of Fraud Study Financial Services and Lending Report US and Canadian Edition,” LexisNexis, 2020, accessed November 7, 2021,

[3] “Mobile Fact Sheet,” Pew Research Center, April 7, 2021, accessed November 7, 2021,

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