Fierce competition, open banking and financial inclusion drive the uptake of transaction analytics in lending

Through explainable machine learning models, behavioural and PD models for the retail banking sector can be created with higher levels of predictiveness.

Many organizations, including data science teams, derail innovation with internal competition that fragments resources and energy.

I predict that Auditable AI and Humble AI will become widely used in 2022, as essential parts of Responsible AI

FICO’s data science labs called interpretable latent feature-based neural networks (ILF NNET) help expose and eliminate biases derived from data showing up in the model.

The Responsible AI Playbook provides a clear path to operationalizing AI deployments. Use it to achieve high-impact business results within important ethical and legal boundaries.

Data scientists need to justify the need for the incremental risk we assume when using more complicated methods to solve a problem - not be open source junkies

Auditable AI provides the documentation and records necessary to pass a regulatory review

FICO’s new Scam Detection Score identifies 50% more scam transactions compared to the standard fraud score

New "State of Responsible AI" report from Corinium and FICO finds that most companies don’t—and are deploying artificial intelligence at significant risk

Whilst the EU have rightly focused on the use of AI and data science strategic leadership, more needs to be done on AI explainability

There is an urgent need for open banking fraud detection, even before banks have sufficient data to train supervised fraud detection models

In part 2 of this series, I take a deep dive into the data science that drives monotonicity and palatability, qualities that help gain trust in AI

Now there is a better way to reduce SAR false positives while stopping money laundering

In part 1 of this series, I explore why it's vital - and difficult - to achieve monotonicity and palatability in certain AI models