With all of the buzz surrounding artificial intelligence (AI) technologies such as ChatGPT, the question becomes “how do we best harness the power of these tools to drive business outcomes?”

In today’s uncertain economic environment, belts are tightening across the board, and investment priorities are shifting away from far-fetched, moonshot projects to practical, near-term applications. This approach means finding opportunities where AI can be practically applied to improve the speed and quality of data-driven decision making.

For banks, these opportunities exist in many areas – from extending credit offers and personalizing customer treatments to detecting fraud and identifying at-risk accounts. However, within the highly regulated financial services industry, leveraging AI to automate these types of decisions adds a layer of risk and complexity.

To get AI-powered decisioning into the hands of the business and drive forward real, meaningful results, technology teams must provide the right framework for developing and deploying AI models responsibly.

What is Responsible AI and why is it so important?

Responsible AI is a standard for ensuring that AI is safe, trustworthy, and unbiased. It ensures that AI and machine learning (ML) models are robust, explainable, ethical, and auditable.

Unfortunately, according to the latest State of Responsible AI in Financial Services report, while the demand for AI products and tools is on the rise, the vast majority (71%) have not implemented ethical and Responsible AI in their core strategies. Most alarmingly, only 8% reported that their AI strategies are fully mature with model development standards consistently scaled.

Beyond the regulatory implications, financial institutions have an ethical responsibility to ensure their decisions are fair and free of bias. It’s about doing the right thing and earning customers’ trust with every decision. An important first step is becoming deeply sensitive to how AI and ML algorithms will ultimately impact real people downstream.

How to ensure AI is used responsibly

Financial institutions need to put their customer’s best interests at the front of their technology investments.

This means having robust model governance practices that ensure enterprise-wide transparency and auditability of all assets – from ideation and testing to deployment and post-production performance monitoring, reporting, and alerting.

It means understanding how models and systems arrive at decisions. AI-powered technology needs to do more than execute algorithms – it must provide full transparency into why a decision was made, including what data was used, how models behaved, and what logic was applied.

A unified enterprise platform provides a common place to author, test, deploy, and monitor analytics and decision strategies. Teams can track how and where models are being used, and most importantly, what decisions and outcomes they are driving. This feedback loop provides critical visibility into the end-to-end impacts of AI-powered decisions across the enterprise.

Unlock a secret advantage with simulation

Designing robust decision strategies and AI solutions often requires some level of experimentation. The development process must include adequate testing and validation steps to ensure the solution meets rigorous standards and will perform as expected in the real world.

With both aggregate and drill-down views, decision testing can reveal how input data moves throughout the strategy to produce an output. This provides useful traceability for debugging, auditing, and governance purposes.

Taking this a step further, the ability to simulate end-to-end scenarios gives users the crystal ball they need to creatively explore ideas and respond to emerging trends. Scenario testing, using a combination of models, rulesets, and datasets, provides a “what-if” analysis for comparing outcomes to expected performance results. This allows teams to quickly understand downstream impacts and fine-tune strategies with the best information possible.

Combining testing and simulation capabilities within a unified platform for AI decisioning helps teams deploy models and strategies quickly and with confidence.

Bring it all together with applied intelligence

With the right foundation, technology teams can create a connected decisioning ecosystem with end-to-end visibility across the entire analytic lifecycle. This foundation accelerates practical AI development and facilitates getting more models into production, ushering in a new age of tackling real-world problems with applied intelligence.

Learn more about how FICO Platform is giving leading banks the confidence they need to move quickly, deploy AI responsibly, and deliver outcomes at scale.

– Jaron Murphy, Decisioning Technologies Partner, FICO