Client story

Scaling payment fraud detection

How a global bank has successfully upped its game using AI to improve its fraud detection capabilities

Stacked image showing a man smiling above abstract of a building

Situation

Rules are performing well but could always be better

Fraudsters are relentless in their attempts to beat existing bank systems. This is why banks much constantly up their game, proactively improving their fraud detection capabilities.

With a host of flexible and configurable rules already in place, overlaying with machine learning can improve detection rates. This is true particularly where data sets are large and identification of fraudulent payments, rather than false positives, needs a greater level of sophistication.

Pain points

  • Fraudsters continually change how they game the system making business rules redundant.
  • Dealing with large data sets requires a greater level of sophistication to avoid false positives.
  • Scoring rules need continuous refinement.

Pain points

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Layer_1

Manual intervention caused low STP rates for certain asset classes.

Layer_2

Inconsistencies in the trade confirmation process increase operational risk.

Layer_3

Regulatory requirements were unmet due to the use of legacy systems not fit for purpose.

No_4

Disparate legacy systems handle multiple asset classes.

Solution

Increasing the sophistication of detection with artificial intelligence

Using a combination of machine learning and a rules-based approach, we have extended the level of automation and improved detection rates for potential fraudulent payments.

Native machine learning puts the power of artificial intelligence in the hands of operational users.

The need to source scarce data scientists is removed and loyal staff are enabled to skill up.

The machine learning model eats a vast amount of data produced by various systems, making sense as it learns and delivers improved confidence scores and a constantly evolving fraud detection system.

 

Vector

Take control of all your inbound and outbound confirmations processes in a single workflow.

Vector2

Ingest data, produce documentation, escalate risks, and manage the indexing and return of confirmations in one standardised workflow across your entire business.

Vector3

Integrate all trade booking platforms, market utilities and resolution portals.

A market leader in confirmations processing, Xceptor is trusted by some of the largest global financial institutions.

Our solution

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Automation

Automation of the trade confirmations process across multi-asset classes.

People

Platform managed by the operations team, enabling them to implement rollouts and rules without the need for IT.

Version control

Our client has control to continuously improve process by refining rules on an ongoing basis.

Audit log

The audit tracking function increases control and reduces risk.

Result

Reduce risk in the fight against fraud

A system that is constantly learning helps banks stay ahead in the fight against fraud.

The system negates the need for data scientists (a costly resource) and enables staff to up-skill in more productive areas of the business.

Reactions to new threats are automatic, saving time and potential exposure to fraudulent activities.

quote

We are delighted to be working with Xceptor in enhancing our ability to detect fraud and provide an even more secure service to our customers.

Chief Operations Officer, Global Bank

Use AI and machine learning to turbocharge your fraud detection processes

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