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With all the buzz surrounding artificial intelligence (AI) technologies such as chatgptSo the question becomes “How do we best harness the power of these tools to drive business results?”
In today’s uncertain economic climate, belts are tightening across the board, and investment priorities are shifting away from far-fetched, moonshine projects. practical, near-term applications, The point of this approach is to find 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 credit offering and personalizing customer behavior to fraud detection and identifying risky 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 put AI-driven decision making into the hands of business and drive real, meaningful results, technology teams must provide the right framework to responsibly develop and deploy AI models.
What is Responsible AI and why is it so important?
Responsible AI It is a standard to ensure that AI is safe, reliable and fair. It ensures that AI and Machine Learning (ML) models are robust, interpretable, ethical and auditable.
Unfortunately, according to the latest The State of Responsive AI in Financial Services The report reports that, while demand for AI products and tools is on the rise, the vast majority (71%) have not implemented ethical and responsible AI into their core strategies. Most alarmingly, only 8% reported that their AI strategies are fully mature with model development standards, which have been continually raised.
Beyond regulatory implications, financial institutions have an ethical responsibility to ensure that their decisions are fair and free from 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 affect real people downstream.
How to ensure AI is used responsibly
Financial institutions need to put the best interests of their customers at the forefront of their technology investments.
This means having strong 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 just execute algorithms – it must provide full transparency as to why decisions were made, including what data was used, how the models behaved, and what logic was applied. Was.
A unified enterprise platform provides a common place to author, test, deploy, and monitor analysis and decision strategies. Teams can track how and where the model is being used, and most importantly, what decisions and results they are driving. This feedback loop provides critical visibility into the end-to-end effects of AI-driven decisions across the enterprise.
Unlock a Secret Profit with Simulation
Designing robust decision strategies and AI solutions often requires some level of experimentation. The development process should include adequate testing and validation steps to ensure that the solution meets rigorous standards and will perform as expected in the real world.
With both holistic and drill-down views, decision testing reveals how input data moves across the strategy to generate outputs. This provides useful traceability for debugging, auditing, and governance purposes.
Taking it 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 “what if” analysis to compare results to expected performance results. This allows teams to quickly understand downstream impacts and improve strategies with the best possible information.
The combination of testing and simulation capabilities within a unified platform for AI decision making helps teams deploy models and strategies quickly and confidently.
bring it all together with applied intelligence
With the right foundation, technology teams can build a connected decision-making ecosystem with end-to-end visibility across the entire analytics lifecycle. The foundation accelerates behavioral AI development and facilitates getting more models into production, ushering in a new era of tackling real-world problems with behavioral intelligence.
Learn more about how FICO Platform Leading is giving banks the confidence they need to move fast, deploy AI responsibly, and deliver results at scale.
– Jeroen Murphy, Designing Technologies Partner, FICO
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