Financial services institutions are increasingly using artificial intelligence (AI) to automate and augment their decisions. But when it comes to which AI algorithms to use, fairness and transparency must factor into the equation.

AI models under examination
To preserve the integrity of the data science profession and safeguard society as a whole, we need to evolve beyond black-box models to create interpretable and explainable models that are actionable and high-performing. These methods will allow financial services to:

• Mitigate bias.
• Understand why models are performing the way they are.
• Build pipelines that are open, understandable, and explainable.
• Make clear to regulators exactly how they’re using ML.
• Give guarantees to regulators of fair risk assessment practices.

Let’s take a closer look at the fight for fairness and transparency in AI. First, a quick primer on the two types of AI we want to encourage firms to pursue: Interpretable AI and Explainable AI.

Interpretable AI
Interpretable AI works through cause-and-effect. Given Data Input 1 and Data Input 2, we can predict the results to be Output A, Output B, and Output C, but we do not know how the model arrived at those outputs. Nevertheless, this method gives us the ability to interpret results and extrapolate codes. One example of interpretable AI is a credit score. Working with known data input—i.e. a person’s family structure, salary, credit history—we can predict what their credit score will be. A person can build interpretability into their existing black-box model through code if they’re willing to apply the methodology.

Explainable AI
Explainable AI refers to models where a person can explain why the model is predicting what it’s predicting, in a format understandable by humans. In other words, we can explain the results because we know the path the model is taking to get to the results.

Considerations for financial services undertaking AI
At a high level, what are two or three key considerations for financial service organizations who want to use AI and ML today to improve products, optimize operations, and better serve customers?

• Collecting and securing data
  The first consideration for any firm looking to use responsible AI is collecting and securing data. Organizations must determine what kind of data they need, whether they’re allowed to collect it, privacy regulations around it, and any potential negative impact in terms of PR if any breach occurs. When it comes to securing data, organizations must consider who needs access to it and how to make sure it’s secure. Data governance solves for these considerations, but data is a whole new universe when it comes to AI.

• Extracting value from data, responsibly
  The second consideration is figuring out how to get actual value out of the model being built—and therefore the underlying data used to build it. Often, companies will come up with an idea and then say, can we build this, or can we build a model that could predict X, Y, or Z? But they very rarely ask, should we build this?

• Cultural readiness for AI
  Thirdly, AI is also culturally driven. If a company isn’t ready for AI, if they haven’t laid the groundwork for this type of innovation in their infrastructure and among their employees, then AI won’t be embraced. Firms can’t just hire data scientists and hope for the best. They have to create a culture that welcomes innovation, is tolerant of experimentation, and is hungry for meaningful data capture in a responsible way.

Source: https://cloudblogs.microsoft.com/

Global iTS is a leading Microsoft Dynamics 365 ERP and CRM Partner with offices all over GCC (Bahrain, Saudi Arabia KSA, Oman “Muscat”, UAE “Dubai”, and Kuwait), with domain expertise in Financial Services Sector Digital Transformation like” Retail Banking, Commercial Banking, Insurance Providers, Private Equity, and Investment Banking.