Algorithm Integrity in Financial Services

We help FS leaders make sure their algorithmic systems do not harm or disadvantage customers, employees, and business partners.

We do this by uncovering potential biases or inaccuracies in algorithmic systems.

Financial Services processes can inadvertently affect customers, employees or third parties negatively.

Whether manual or automated, they often rely heavily on data or algorithms (a.k.a AI systems).

Examples include:

• Automated fee or payment calculations without human intervention (e.g., automated bank fee postings)
• 3rd party commission calcs that you use for external payments (e.g., to brokers for loan origination)
• Fraud risk rules and models, flagging potentially fraudulent interactions (e.g., fraudulent insurance claims)
• Reports with data from multiple sources, that you rely on to make operational decisions (e.g., sales KPIs)

They can involve complex data flows, transformations, reports and/or algorithms.

Without proper oversight, these processes can inadvertently lead to unfair treatment or harm to customers.

For example:

• Biased data/models could result in unfair loan rejections for certain demographic groups
• Inaccurate fee calculations might overcharge/undercharge customers
• Incorrect commission calculations can underpay/overpay third parties
• Aggressive fraud detection could wrongly flag legitimate activity.

That's where we come in

We examine these processes in detail. 

We analyse the data inputs, algorithmic logic, and outputs to ensure they're fair and accurate.

By doing so, we help you maintain trust, protecting customers and third parties from unintended harm or disadvantage.

Our approach is outlined further here.

We work with senior leaders:

• In insurance companies, banks and credit unions
• Who want to know that their algorithmic systems are accurate and fair
• Who want reviews (rather than just "need" reviews), as explained in this article.

If this describes you, schedule a call to explore how we could work together.