Explainable AI

When we consider the potential that Artificial Intelligence (AI) and Machine Learning (ML) models bring to the table, it’s not surprising to see why they are gaining massive traction in the global fintech industry. However, the “black box” AI still remains ambiguous due to its lack of explainability about how financial decisions are made, particularly in the process of credit scoring — resulting in distrust of AI-powered solutions. The Explainable AI (XAI) model takes away the problem by eliminating these ambiguities, thereby providing transparency, accountability, and fairness. Let’s find out how.

Why AI Explainability Matters

It is important for fintech companies to understand the AI decision-making processes without relying on them blindly. The emergence of explainable AI can help humans understand and explain ML algorithms and neural networks.

This makes it imperative for fintech companies to monitor and manage model results to improve AI explainability while measuring the business impact of using such algorithms in financial risk management and audit. Explainable AI helps strengthen end-user trust and productive use of AI. It also reduces compliance, legal, security, and reputational risks for production AI.

Developing an AI model for the use of the fintech industry can help forecast events and rate transactions based on prior patterns. Once the model is in use, it receives millions of data points, then interacts in billions of different ways to produce results quicker than any combination of human effort.

The danger is that the ML model can be producing these results in a closed system that is only understood by the group that created the model from scratch. In 2021, 32% of financial executives responding to the LendIt annual study on AI cited that the lack of explainability is their second highest concern after regulation and compliance.

How Explainable AI Works

As AI becomes integral in the fintech industry, explainability has become important to building customer trust. Although “black box” solutions allow users to know the input and the final output, it obscures the process of how a decision came to be. XAI also called the “white box” model, helps transmit decision-making information to outputs and makes them obvious to FI’s users. These exhaustive data are then examined by users to explain and validate the results.

There are some setup methods for XAI technology. Predictive accuracy addresses technical needs, while decision comprehension addresses human needs.

  1. Prediction Accuracy

Prediction accuracy can be determined by running a simulation and comparing the XAI output with the results in the validation dataset.

2. Decision-making Comprehension

This is the human factor.  Explainability can be achieved by training teams to work with AI so they can understand how and why AI makes decisions.

Explainable AI gives fintech companies more clarity on their AI governance while assisting them in providing transparency and building trust with their customers. XAI comes into play to make humans comprehend AI models without compromising performance or prediction accuracy.

How to Implement Explainability in Your App

The process of implementing XAI across fintech organizations involves many aspects and steps, including the development of models, interaction with various stakeholders, governance procedures, and involvement of outside vendors. For example, the following objectives should be at the forefront of banks’ XAI implementation:

  1. XAI should make it easier to comprehend whether features or interactions had an impact on model predictions as well as the processes a model has taken to come to a conclusion.
  2. Explanations should detail a model’s benefits and drawbacks as well as potential future behavior.
  3. Customers should be able to comprehend explanations, which should be presented in a simple and intuitive manner according to the preferred language of the target audience and their technical proficiency.
  4. XAI methods should reveal insights on model behavior, in addition to how an organization will use the results.

To kickstart a project and turn a vision into reality, fintech companies need the support of an experienced software development team. For example, at MobiDev, the financial software development process always starts with the discovery phase which helps to understand the project requirements and create a clear roadmap for further product development. Then AI engineers consult on how to apply machine learning algorithms to achieve project goals.

Implementing XAI models as a formal regulation helps fintech companies get closer to accomplishing these objectives. From the pre-modeling phases to the monitoring and evaluation phases following the deployment, this will entail implementing new policies and approaches.

Challenges and the Future of Explainable AI

Limitations and Challenges

Although XAI research has witnessed a significant rise, fintech companies still experience conundrums introducing explainability into the AI pipeline. Some explanations might not support adjustments that can be made to interest rates, repayment plans, and credit limitations, thereby, neglecting consumers’ preferences for various loan arrangements.

Some organizations have voiced fear that explainability might enable their competitors to reverse-engineer their ML models, thereby disclosing the “secret element” behind their proprietary algorithms. Also, they have drawn attention to the possibility that XAI could make it simpler for outsiders to manipulate their models or launch aggressive attacks that break them. Without looking at the reasoning behind an AI’s conclusions, it is challenging to say whether it is trustworthy or not.

The Future of XAI

Despite all limitations, XAI technology is seeing exponential growth in the fintech industry and it’s only getting started. A survey by NMSC report estimates that the worldwide XAI market would be worth $4.4billion in 2021 and $21.0billion in 2030, with a CAGR of 18.4% from 2022 to 2030.

One of the major reasons that prevent many people from embracing AI is the lack of explainability and trust. But the gap has been bridged, thanks to explainable AI. Fintech companies can now comprehend every form of data-driven decision-making. When working with an experienced team of AI developers, all these challenges can be overcome and you will get a quality AI-powered solution.


Anastasiia Molodoria

AI/ML Team Leader at MobiDev



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