ai’s SmartSuite of machine-learning technology gives banks the opportunity to simply write their own rule sets into any fraud platform, including ai’s rules engine RiskNet®. By using machine-learning, banks can automate their fraud prevention, mitigating ACH fraud regardless of how it is perpetrated including account takeover, ‘man in the middle’ fraud and/or social engineering.

SmartScore®, one of six products within ai’s SmartSuite, creates neural models using artificial intelligence and automated machine-learning techniques, to recognise patterns and trends in fraud. Providing transaction risk scores to be used in conjunction with user-defined rules and parameter-mapping.

“ai is tirelessly working on ways to facilitate and protect both faster payments and alternative payments by providing our clients with self-service SaaS or software solutions that automate manual processes and allow the fraud teams to focus on more important business problems.” Mark Goldspink, CEO, ai.

With its unique multi model capabilities, SmartScore® enables users to create Neural Models specific to a particular fraud type, customer segment or payment method, including ACH. By constantly refreshing the data available, SmartScore® provides an up to date and accurate risk score based on current trends, ensuring that RiskNet® or any third-party fraud platform users are not reviewing unnecessary alerts.

About ACH Payments

As of September, last year (2016), credit-based ACH payments are now being settled within the same day. This includes direct deposit, payroll, person-to-person and vendor payments. This year, 2017, ACH debit-based payments will follow suit. 

Same-day credit-based ACH payments create significant fraud exposure and criminals are leveraging, this will increase exponentially when same-day debit-based payments are brought in. Instead of having two to five days to detect fraudulent transactions institutions will be faced with as little as two hours to analyse suspicious activity.

Financial institutions need to change their processes and risk strategy to tackle this new fraud risk and to prepare for future increased fraud risk. ai has created ACH specific neural models that combat this growing problem within financial institutions.

ai’s advanced data visualisation tools bring the data to life allowing for a granular level of interrogation, highlighting potential risks hidden in large amounts of data. Their network visualisation technology identifies commonalities and consolidate ongoing investigations.

Utilising ai’s suite of self service, automated, and highly scalable products in a SaaS environment, provides a proven multi layered preventative solution to ACH fraud for your business.

Southampton University and Leuven University partnership 

As part of their ongoing fight against payment fraud, ai has partnered with two leading universities, Southampton University and Leuven University in Belgium, to form a Research Council to investigate the benefits of using true machine deep learning in fraud prevention. The Council’s research will further strengthen ai’s position at the forefront of the payments industry. 

Tom Myles, Chair of the Research Council and CTO at ai says: “One of the team’s focuses will be the adoption of true machine deep learning. This is the movement away from supervised to an unsupervised self-learning system, with the purpose of fully automating fraud prevention activities for all payment types.”