
Preventing fraud at banks and credit unions
Guardian Analytics, a behaviour-based fraud prevention solutions firm, is launching FraudMAP ACH (view press release). Using a behaviour-based anomaly detection technology designed to prevent fraud at banks and credit unions in the online and mobile banking channels, FraudMAP ACH analyses ACH batches and transactions and prioritises the highest risk payments for review. The service has been created to increase operational efficiency and reduce the risk of lost profits and clients.
‘With ACH transaction volumes growing & criminals more eloquently tampering with ACH files, financial institutions are expending significant resources monitoring for fraudulent transactions & in many cases failing,’ said Craig Priess, Guardian Analytics. ‘This is not a sustainable model. FraudMAP ACH fundamentally changes both the manner in which & how successfully a financial institution manages ACH payments risk.’
The company claims that FraudMAP ACH is the industry’s first complete behavioural analytics-based fraud prevention solution for ACH transactions. NACH released Sound Business Practices for Implementing Provisions of the Supplement in July 2012 in which they reinforce the need for anomaly detection capabilities called out in the 2011 FFIEC Guidance Supplement. The practices encourage FIs to ‘monitor accounts for unusual and out-of-pattern transaction flows.’ FraudMAP delivers these capabilities by:
- Using the company’s proprietary analytics, Dynamic Account Modelling, FraudMAP ACH monitors originator and recipient behaviour for anomalies relative to typical behaviour.
- Automatically identifying high risk batches and individual transactions buried deep within very large ACH files, including payments where only details of single line items have been modified.
- Eliminating the need to maintain rules, limits, and negative lists and then manually comb through long exception reports.
- Focusing payment staff efforts on the most suspicious batches and transactions by rank ordering alerts and providing rich details on the unusual activity and client’s payment history.
‘The FS-ISAC Account Takeover Task Force survey by the ABA shows the instances of corporate account takeovers are significantly increasing year over year,’ said Bill Nelson, Financial Services Information Sharing & Analysis Center (FS-ISAC). ‘The study shows that investing in advanced layers of fraud prevention reduces risk & stems losses from attacks on ACH payments.’ The 2012 Business Banking Trust Study by Ponemon Institute highlights that financial institutions should consider not only fraud losses but the complete impact of fraud, which include operational losses and customer churn. Findings in this annual study specific to the ACH channel include:
- 73% of corporate account takeover instances led to money leaving the bank before anyone noticed.
- 56% of businesses said it would take only one fraud incident for them to lose confidence in their financial institutions ability to protect their accounts.
- Of businesses that were hit by fraud, 38% took some or all of their businesses elsewhere as a result. So, banks are losing customers.
- When a business loses money through a fraudulent ACH transaction, the bank reimburses some or all of the money 54% of the time. So, banks are losing money.
Whitepapers
Related reading
Central banks best suited to issue digital currencies
By Aaran Fronda A recent report by the Official Monetary and Financial Institutions Forum (OMFIF) said that central banks rather than private ... read more
Instant payments: innovations inbound for corporates
In 2020, instant payments look set to continue their current trajectory to become the biggest trend in payments. While these schemes already offer numerous benefits to corporates, leveraging innovations such as APIs and request to pay will go some way to unlocking their full potential, argues Michael Knetsch
Obstacles exist for banks to meet ECB’s instant payments goal
The cost of joining instant payment platforms will be one of many hurdles banks and payment services providers must overcome to meet ... read more
Banks must be aware of “biases” in data used to train ML models
Financial institutions need to be conscious of biases in the historical data that is being used to train machine learning (ML) models, ... read more