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Why is Explainable AI important in Finance?

Explainable AI in Finance

Before we delve into explainable AI in finance, it only makes sense to grasp the idea behind the phrase “Explainable AI.”

Explainable AI refers to Artificial intelligence, which produces comprehensive results assimilable by humans. It is often referred to as the direct opposite of the black box concept in machine learning which projects results that are often unexplainable by even the designers of the AI.

In a nutshell, explainable AI is understandable AI hence its potential application in the financial sector. Its applications may be attributed to its unique feature of decision traceability. Owing mainly to the potentially detrimental effects of mistakes by AI in a volatile industry such as finance, financial institutions have been a little laid back with regards to the implementation of AI to ensure a more effective industry.

Such errors could emanate from the AI black box concept; thus, the growing suggestions within the financial community to apply explainable AI in finance. Explainable AI can show reasoning and errors and give room for using performance improvement and risk mitigation tools.

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A typical example of the loopholes of the AI black box concept is Apple’s digital card which was accused by the populace of discriminating against women as the card for reasons unknown offered more credit to men than women.

By applying explainable AI in finance, we can be sure of more transparent outcomes aligned with regulatory requirements. In a nutshell, explainable AI (XAI) explains why specific results were reached and how these outcomes came to be.

How does explainable AI function?

XAI provides a justifiable reason for every outcome. This means that if a client’s loan request is turned down, as is the case when dealing with humans at the bank, the client may request reasons why their application was turned down and suggestions on meeting the minimum requirements for the loan. 

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During this query, the XAI will consider several factors. These factors include the operation method of the model, the algorithm being used, and what factors directly affect the results being shown.

Comprehensive explanations

Thanks to these factors, we can explicitly tell if the XAI is functional or defective based on the factors mentioned above’ answers.

Since we are talking about machines for the most part, and most people still attribute the generic ideas of garbage in garbage out to modern-day AI, it is essential to state that the outcomes shown by XAI are entirely explainable.

This means to say that for every loan given or rejected, there must be a meaningful reason given by the system to justify the action undertaken. While the definition of significance in this context is not exactly objective, it is expected that the end-user can comprehend the information provided.

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Clear and concise explanations and limited knowledge base

By giving clear and concise explanations, it becomes a lot easier to verify the actions taken by the system. Hence, there’s room for accountability which generally improves trust among users.

Exceptions must exist as well in a bid to curb unrealistic outcomes. This further mitigates the risks of fatal errors as errors are limited within certain acceptable thresholds.

Implementing XAI in Finance

Certain factors must be put in place if the financial sector will actively adopt XAI. Some of these factors include:

Inadequacies in Customer Onboarding: Due to weaknesses in customer onboarding processes and the highly competitive marketplace, financial institutions lose a lot of money. There is room for more extensive checks by implementing explainable AI, and risks are seriously mitigated to ensure less expensive operational costs.

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Risk Mitigation: By checking current trends and comparing these trends with past inconsistencies and red flags, fraud and other malpractices are often easier to spot by explainable AI systems in the financial industry.

Better forecasts: Based on set key indicators, explainable AI, if implemented, is sure to make accurate projections about the finances and performance of the financial institution in question.

It also provides a reference point for eventual outcomes to be measured to promote accountability within financial institutions.

Cash Management: A rather esoteric challenge most financial institutions face is cash management. Justifying the right amount of cash to be made available by considering volatile factors like ATM withdrawals, loan demands, seasonal withdrawals, and events can be quite a task. Thankfully explainable AI can project the cash needs based on insights and historical data.

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All this talk about Reinforcement Learning and Explainable Reinforcement Learning

Reinforcement learning (RL) provides leeway to any system based solely on its solution strategy. Due to the sheer number of available strategies towards the same goal, a level of analysis is conducted to choose the best possible approach to complete a given task.

Specific solutions for this involve adding transparency to typical RL operations. This is often called explainable reinforcement learning (XRL).

Explainable reinforcement learning (XRL) is fundamentally made up of two possible solutions. These solutions are interpretability and algorithmic transparency. To effectively utilize these solutions, they must be made more comprehensive to ensure better regulatory practices by respective bodies. 

A more comprehensive solution would mean adoption by consumers, which implies a complete and utter understanding of the principles of these systems by all parties.

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To better understand the concept of explaining reinforcement learning (XRL), we can look at state representation learning (SRL) which refers to a system of feature learning that mitigates dimensionality of a system in a bid to ensure seamless understanding of complex systems by RL systems.

In a nutshell, state representation learning (SRL) can identify the why and how of certain decisions.

Currently, a more significant percentage of methods used in XRL exist in robotic and image recognition applications. However, this is expected to expand to other areas, especially the financial sector.

This is arguably one of the reasons why it is essential for an explainable AI. AI that provides reasons and solutions is critical since the financial system is a rather sensitive sector. 

Over the years, transparency in algorithms has become a pressure point within the AI industry due to the 2010 flash crash events. Hence, the increasing need for explainable AI especially pertains to sensitive and volatile sectors like the financial sector.

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