Thoughts on Overcoming modern digital AI challenges and Temenos Multifonds - Explainable AI (XAI)

Just read a very interesting article (1) in Fintech Times from Polly Jean Harrison "How XAI Looks to Overcome AI’s Biggest Challenges" that I recommend, where Evan Chrapko, Chairman and CEO of Trust Science, an Edmonton-based AI firm currently dedicated to reshaping modern data analysis techniques, particularly in the area of credit scoring shares his thoughts on how XAI looks to overcome AI’s biggest challenges.

I immediately thought about our work on Temenos Multifonds XAI a Highly transparent and explainable AI works to help accounting teams work quicker and smarter with a reduced number of false positives and all that we have accomplish and wish to do in the future.

According to the article with the proliferation of artificial intelligence (AI) technologies in different financial and IT industries, AI’s potential has only increased. As AI tools become more advanced, there is an increased possibility that most of the decision-making computations are done in a “black-box” with no human explainability. Explainability is the key to customer trust, especially in the field of credit scoring – customers will not trust a machine’s decision if they don’t understand the complex decision-making process. XAI equips the AI learning models with transparency, fairness, accountability and explainability. With Explainable Artificial Intelligence (XAI), companies in the 21st century can solve their “black-box” problem and help customers understand how and why they received the particular credit score. 

XAI refers to the tools and frameworks that can make the predictions and decisions made by machines understandable to humans. XAI widens the interpretability of AI models and helps humans to understand the reasons for their decisions. Artificial Intelligence (AI) is becoming a key part of our day-to-day lives and business operations. However, the adoption of AI across business sectors has not come without its challenges. Most companies today still  work with what are known as “black box” AI systems. These opaque models make it difficult to understand how decisions have been reached; they rely on data and learn from each interaction, thus can easily and rapidly accelerate poor decision making if fed corrupt or biased data.

Bias detection 

The fintechtimes article referes that one of the major concerns of using AI tools is the reproduction of bias present in the traditional data. AI feeds on large amounts of data from different sources and if the data fed into the AI tools is biased, there is a possibility that the AI will replicate those biases into the results. Research has shown that XAI can generate a fairness report that can measure the degree of bias in the results. XAI can be used to explain why a user or group is treated unfairly with the given data. Some data scientists have proposed a fair-by-design approach to develop ML models that have less bias and have explanations understandable by humans. Such an XAI can pinpoint the reasons for biases in the system and thereby, help developers to fix them. And this is one of the strongest points of Temenos Multifonds XAI.

With XAI solutions we seek to make non-expert users and customers understand why and how a certain decision was made, thus, maintaining a high level of transparency as well as audit and regulatory adherence.

Overcoming the AI challenge

XAI models are highly transparent and explain, in human language, how an AI decision has been made. The explainability not only provides explanations for its decisions, but helps users identify and understand underlying issues, so they leverage results to identify the root cause of a problem and improve their operational processes. Crucially, they do not solely rely on data, but can be elevated and augmented by human intelligence. These models are built around the relationship between cause and effect, creating space for human sensibility to detect and ensure that the machine learning is representative, comprehensive, complete and deals with all possible scenarios, and if it’s doesn’t, it allows  the necessary changes to be made.  XAI not only supports increased efficiency and automation but, by virtue of being transparent, it provides a model that businesses can trust entirely to support their operations, with full auditability. 

Let’s look at an example

Recent market volatility has been creating new challenges for fund accounting teams, and many find themselves spending more time managing control breaks and checking false positives in order to finalise their NAV calculations. Using Temenos XAI fund accounting teams can clear exceptions (for example, price variation exceptions) more efficiently by prioritising those breaks with XAI scores or automatically justifying a break if the score/probability of being a false positive is high enough.

The XAI model score is based on price history, corporate action history, benchmark data, security master data and historic exception data stored in Temenos Multifonds. With the data points for new price variation exceptions, the model then calculates the likely probability that each individual break is a real issue or a false positive, and importantly, the model’s result provides an explanation on how it came to this decision.

By leveraging this technology, accounting teams can work quicker and smarter with a reduced number of false positives, as well as the ability to prioritise exceptions based on XAI results, saving them valuable time on manual investigation. Our analysis of the price variation control specifically suggests that over 80% of control failures end up being justified as false positives. 

Key features for a this particular XAI integration success

• Highly transparent and explainable
• Removes human error from the decision making process
• Creates new efficiencies in the NAV calculation process
• Elevates existing Multifonds automation
• Cloud-native hosted
• Integrated via APs

Make sure you take a look at XAI solutions for your Fintech/Bank and of course a look at Temenos Multifonds XAI

Happy New 2022!

PS: Kudos for Polly Jean Harrison, FIntech Times and van Chrapko for the great article.

More on Temenos Multifonds XAI: Temenos Multifonds - Explainable AI (XAI)

read it here: How XAI Looks to Overcome AI's Biggest Challenges | The Fintech Times

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