New AML scores reduce false positive alerts by 50% while detecting 100% of known money laundering transactions, and discover new aberrant, potentially risky behaviors

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

The ability to offer full control of affordability strategies and models to ensure agility, flexibility and transparency really matters — because it’s getting ever more complex

Talking with A-level students at Winstanley College, Wigan, gave me hope for the future of data science

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

From South America to South Africa, businesses are using centralized decisioning to connect with customers

Open Banking has the potential to empower more choice for millions more consumers in Europe and Africa - better analytics and breaking down banking silos will be key

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

Access to the broadest datasets available, investment in the right technology and application of AI and automation can be a win-win for lenders and SMEs seeking funding

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