How XAI Enhances Predictive Analytics in Banking

How XAI Enhances Predictive Analytics in Banking

Explainable AI (XAI) provides the banking industry with the explainability it needs when applying machine learning to any application. Speed and precision are table stakes for adopting AI technologies vs. traditional predictive analytics in the banking industry. However, explainability not only brings transparency, but also insight. Here are 6 ways explainable AI, through speed, precision and insights, enhances predictive analytics for banks.

Explainable AI not only provides the ability to predict fraud, it can tell you why – XAI can determine what specific factors are associated with every fraud transaction prediction. This is very valuable insight in that it tells you:

  • What factors in order of importance are associated with a prediction
  • How many varieties of fraud occur by prediction type based common characteristics
  • What distinctly defines emerging fraud patterns and trends
  • How to prevent new fraud transactions from occurring at a business policy level

Traditional predictive analytics in banking has struggled to provide consumers with explanations on credit decisions. However, explainable AI is now able to meet this high bar. And since it’s unknown when a consumer may demand an explanation, the underlying factors associated with a machine learning-based credit decision must be indexed in a way that enables retrieval at speed and scale in the host country where the predictions are being generated.

XAI supports this critical need while providing:

  • The underlying decision factors that can automatically be translated to credit-based reason codes based on order of importance
  • New patterns or trends emerging in decision risk based on ingesting wider data sets
  • Clustering of decision type across geographies for credit analysis
  • Provides mirroring of existing algorithms to reveal the explanation behind their predictions

Many banks already have machine learning algorithms running for a wide range of uses and applications. However, when examining when and why those algorithms make inaccurate predictions, knowing what is contributing to the inaccuracy is highly valuable for tweaking the models accordingly. XAI can act as a debugger to make this continual examination and performance upgrade possible for existing algorithms.

  • Reveal the factors behind erroneous predictions so that fixes can be made
  • Continuously analyze and group predictions with common errors
  • Maintain existing algorithms in an automated manner saving time and cost
  • Detect algorithmic bias
  • Confirm a models efficacy compared to alternatives

Banks need to upgrade their communication relevancy with customers. Relevancy in customer communications has historically been low, particularly in outbound marketing campaigns. Where traditional predictive analytics in banking has fallen short, XAI is able to reveal the true drivers of predicted purchase behavior based upon the past behavior of existing buyers. The ability to expose these factors can fundamentally change the way banks market to their buyers. XAI can completely change the relevancy of campaigns:

  • Reveal precisely what motivates a predicted buyer to purchase
  • Cluster similar predictions together into Dynamic Predictive Segments
  • Enables tailoring of creative, message and offer to match predictive segments
  • Increase response and conversion rates through increased targeting precision and relevance

In addition to increased campaign relevancy, personalizing the customer experience in online portals and applications is now a basic consumer expectation. Explainable AI provides the ability to use the insights gleaned from each prediction to truly personalize every experience in a powerful way.

XAI enables personalization that:

  • Leverages factors in order of importance to make customer recommendations more relevant
  • Integrates recent buyer activity to apply recommendations quickly effectively
  • Can handle speed and scale of real-time recommendations that span broad touchpoints consistently

When it comes to machine learning in financial services, being able to perform deep analyses of risk and opportunities across an entire customer portfolio on a daily basis requires powerful AI tools. Explainable AI is a perfect fit for the heavy lifting of analyzing multiple components of a customer portfolio to answer key questions, and to provide, the Why behind the answers:

  • Track portfolio changes based on driving factors of change
  • Ingest broader market data to uncover new causes of changing portfolio behavior
  • Uncover new hidden opportunities for increased cross selling and upselling
  • See new customer segments for the first time at a highly granular level

XAI can do a lot of things for a bank, but these applications are tailor made for leveraging the power of explainability. The sheer impact of increased precision alone coupled with explanations that enable fast business decisions, is a powerful combination for enhancing predictive analytics in banking.

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