# Why Explainable AI (XAI) will have a major role in financial services
Author:  Pal Sinha, Barnali 
Author URL: https://financedigest.com/author/pal-sinha-barnali
Published: 2020-11-04
Category: TECHNOLOGY
Category URL: https://financedigest.com/category/technology
Meta Title: The Value of Explainable AI in Financial Services
Meta Description: Discover how Explainable AI (XAI) is revolutionising financial services by enhancing innovation, model performance, compliance and competitive advantage.
URL: https://financedigest.com/why-explainable-ai-xai-will-have-a-major-role-in-financial-serviceshtml

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_By **Alexei Markovits,** AI Team Manager, Element AI_

Ground-breaking advances in artificial intelligence (AI) are changing our world all the time. AI systems are used to trade millions of financial instruments, assess insurance claims, assign credit scores and optimise investment portfolios.

Yet while we may benefit from these advances, we also need a framework that helps us understand how AI arrives at its findings and suggestions. It is essential so we can establish trust and deploy those outputs to their [full potential](https://www.financedigest.com/embedded-finance-is-the-new-norm-in-banking-but-what-is-needed-to-unleash-its-full-potential.html "Embedded finance is the new ‘norm’ in banking, but what is needed to unleash its full potential? ").

The processes behind AI are not always obvious. Many of today’s advanced [machine learning](https://www.financedigest.com/transforming-insurance-through-artificial-intelligence-and-machine-learning.html "Transforming Insurance Through Artificial Intelligence and Machine Learning") algorithms that power AI systems are inspired by the processes of the human brain, but are constrained by their lack of human ability to explain actions or reasoning.

For this reason, an entire [research field is now working](https://www.financedigest.com/the-pandemics-impact-on-working-mothers-new-research-reveals-that-two-thirds-have-stopped-working-or-now-work-less.html "The Pandemic’s Impact on Working Mothers: New Research Reveals That Two-Thirds Have Stopped Working or Now Work Less") towards describing the rationale behind AI decision-making. This is known as Explainable AI (XAI). While modern AI [systems demonstrate performance and capabilities far beyond previous technologies](https://www.financedigest.com/sensor-based-glucose-measuring-systems-market-by-2028-with-technological-advancements-growth-of-industry-overview-and-dynamics-with-affecting-factors-2.html "Sensor Based Glucose Measuring Systems Market by 2028 With Technological Advancements, Growth Of Industry, Overview and Dynamics with Affecting Factors"), practicality and legal compliance can inhibit successful implementation.

For organisations looking to utilise AI effectively, XAI will be a key deciding factor due to its ability to help [foster innovation](https://www.financedigest.com/financial-services-tech-tools-to-foster-innovation.html "Financial Services: tech tools to foster innovation"), enable compliance with regulations, optimise model performance, and enhance competitive advantage.

**Explainable AI and its value in** [financial services](https://www.financedigest.com/can-financial-services-brands-ever-be-credible-on-social.html "Can financial services brands ever be credible on social? ")

In financial [services](https://www.financedigest.com/integrated-finance-the-advantages-of-using-financial-infrastructure-as-a-service.html "Integrated Finance: the advantages of using financial-infrastructure-as-a-service"), the techniques of explainability are becoming especially valuable. When it comes to financial data, many service providers and consultants may already be aware of the low signal-to-noise ratio that is typical of this data, which in turn [demands a strong feedback loop between user](https://www.financedigest.com/sodium-tetraborate-market-key-players-end-user-demand-and-consumption-by-2030.html "Sodium Tetraborate Market Key Players, End User, Demand and Consumption by 2030") and machine.

AI solutions that are designed without human feedback capabilities run the risk of never being adopted [due to the persistence of traditional approaches that rely on domain expertise and experience from years](https://www.financedigest.com/child-resistant-single-dose-pouches-market-one-the-most-booming-industry-in-upcoming-years-due-to-global-demand-by-2031.html "Child Resistant Single Dose Pouches Market | One the Most Booming Industry in Upcoming Years Due to Global Demand by 2031") gone by. AI-powered [products that are not auditable will simply struggle to enter the market](https://www.financedigest.com/bagasse-tableware-products-market-will-top-us-3-75-bn-by-2031-as-consumers-proclivity-for-eco-friendly-and-single-use-product-purchase-grows.html "Bagasse Tableware Products Market will top US$ 3.75 Bn by 2031 as Consumers’ Proclivity for Eco-Friendly and Single-Use Product Purchase Grows") as they’ll face regulation issues.

**Marketing forecasting and** [investment management](https://www.financedigest.com/mg-appoints-former-head-of-axa-investment-managers-andrea-rossi-as-new-ceo.html "M&G appoints former head of AXA Investment Managers Andrea Rossi as new CEO")

![Alexei Markovits](https://prod.superblogcdn.com/site_cuid_cm5qst7v3003gwirgwqtxn8i8/images/alexei-markovits-high-res-450x415-1736838745275-compressed.jpg)

Alexei Markovits

Time series [forecasting](https://www.financedigest.com/italy-slashes-2023-growth-forecast-but-sees-public-finances-improving.html "Italy slashes 2023 growth forecast but sees public finances improving") methods have grown significantly across financial services. They are useful for predicting asset returns, econometric data, [market volatility](https://www.financedigest.com/puma-confirms-full-year-outlook-flags-market-volatility.html "Puma confirms full-year outlook, flags market volatility") and bid-ask spreads – but are limited by their dependence on historical values. As they can lack disparate, meaningful information of the day, using time series to predict the most likely value of a [stock or market](https://www.financedigest.com/bitcoin-falls-to-22-month-low-as-stock-markets-tumble.html "Bitcoin falls to 22-month low as stock markets tumble") volatility is very challenging.

By complementing such [models with explainability](https://www.financedigest.com/gary-mcgaghey-explains-how-cfos-can-strengthen-decision-making-processes-and-reimagine-the-finance-model.html "Gary McGaghey Explains How CFOs Can Strengthen Decision-Making Processes and Reimagine the Finance Model") methods, users can understand the key signals the model uses in its prediction, and interpret the output based on their own complementary view of the market. This then enables a real synergy between [finance specialists’ domain expertise and the big data-crunching abilities of modern](https://www.financedigest.com/why-do-modern-finance-functions-strive-for-automation.html "WHY DO MODERN FINANCE FUNCTIONS STRIVE FOR AUTOMATION?") AI.

Explainability techniques also enable human-in-the-loop AI solutions for portfolio selection. An investor might find that they choose not to pick the suggested portfolio with the highest reward if the level of risk appears too great. On the other hand, a system that provides a detailed explanation of the risks, such as how they could be uncorrelated with the market, is a powerful addition to [investment planning](https://www.financedigest.com/samsung-elec-breaks-ground-on-new-chip-rd-centre-plans-15-billion-investment-by-2028.html "Samsung Elec breaks ground on new chip R&D centre, plans billion investment by 2028") tools.

**Credit-scoring**

Assigning or denying [credit to an applicant is a consequential decision that is highly regulated](https://www.financedigest.com/swiss-national-bank-seeks-banking-regulation-review-after-credit-suisse-crash.html "Swiss National Bank seeks banking regulation review after Credit Suisse crash") to ensure fairness. The success of AI applications in this field depends on the ability to provide a detailed explanation of final recommendations.

Beyond compliance, the [value](https://www.financedigest.com/how-embedded-insurance-can-drive-growth-and-value-for-the-insurance-and-finance-sectors.html "How Embedded Insurance Can Drive Growth and Value for the Insurance and Finance Sectors") of XAI is seen for the client and financial institution in different ways. Clients can receive explanations that give them the information they need to improve their credit profile, while service providers can better understand [predicted](https://www.financedigest.com/the-digital-first-world-financial-services-predictions-for-2022.html "The digital-first world: financial services predictions for 2022") client churn and adapt their services.

Through use of XAI, credit-scoring can also help with reducing risk. For example, an XAI model might provide an explanation of why a pool of assets has the best distribution to minimise the [risk of a covered bond](https://www.financedigest.com/bank-of-england-says-it-will-unwind-bond-market-intervention-once-risks-have-subsided.html "Bank of England says it will unwind bond market intervention once risks have subsided").

**Explainability by design**

Since AI [solutions are now evolving beyond proof-of-concept to deployment at scale](https://www.financedigest.com/veritone-launches-marvel-ai-a-complete-end-to-end-voice-as-a-service-solution-to-create-and-monetise-hyper-realistic-synthetic-voice-content-at-commercial-scale.html "Veritone Launches MARVEL.ai, a Complete End-to-End Voice-as-a-Service Solution, to Create and Monetise Hyper-Realistic Synthetic Voice Content at Commercial Scale"), it has become essential to recognise the importance of prioritising explainability to power human-AI collaboration and to satisfy audit, regulatory and adoption requirements. A user-centric approach, and the imperative for transparency across AI systems together reinforce the need for explainability to be a part of that cycle. All the way from the initial process of building a solution, right to the system integration and use.


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