Understanding the Role of Explainable AI in Embedded Finance Compliance
While embedded finance platform providers don’t necessarily have to be fully regulated, there is a level of compliance expected of them when providing financial products to businesses and consumers. This blog explores the role of explainable AI in the context of embedded finance compliance, the challenges, best practices and use cases.Return to blog posts
What do you get when you integrate third-party banking-like services into a non-financial customer journey at the point of need? This might sound like the opening line to a corny joke but the answer is no laughing matter: embedded finance. By removing friction from the consumer experience, and creating growth opportunities, competitive advantage, and operational efficiencies, it presents a significant opportunity to fintech providers and non-financial companies – but a challenge inherent to financial services remains: regulatory compliance.
Businesses that embrace embedded finance must be empowered to strike a balance between delivering a seamless customer journey and achieving regulatory compliance – fail and they will be exposed to financial penalties and reputational damage.
Rather than allowing regulatory compliance to slip through the cracks during integrations, embedded finance providers are leveraging explainable artificial intelligence (XAI) to manage this vital requirement – innovation that enhances the delivery of frictionless financial services.
What is XAI?
AI empowers financial service providers to harness the large volumes of data generated in financial transactions – and the benefits are compelling: identify patterns, make predictions, create rules, automate processes, communicate more efficiently, obtain a holistic view of the customer, and provide timely support. But there’s an inherent problem that presents ethical and regulatory challenges: the developers who build AI decisioning models can’t always explain how it arrives at the outcomes or which factors had the biggest influence.
The emerging field of XAI is providing embedded finance providers with a platform to overcome issues of transparency and trust by lifting the veil on opaque AI models. This set of processes and methods makes AI models more explainable, intuitive, and understandable to human users without sacrificing performance or prediction accuracy.
XAI in the context of embedded finance compliance
XAI augments AI’s innate ability to process large volumes of data expeditiously with transparency. This allows it to cut through the regulatory noise and provide information that can be trusted to maintain compliance.
A single embedded finance regulatory framework does not currently exist, amplifying the need for clarity from a compliance perspective. While the regulatory policymaking process is moving at a glacial pace compared to the exponential growth of embedded finance technology, specific rules and regulations are emerging and more will follow. For example, the recent updates to Buy-Now-Pay-Later (BNPL) regulations in the UK now require providers to perform credit checks.
XAI automatically horizon scans the regulatory landscape for new rules and regulations – or tweaks to existing ones – updates stakeholders and produces performance data that underpins preventive action if processes shift towards non-compliance. Crucially, there is no ambiguity around the steps taken to reach its conclusions.
For example, businesses that embed responsible lending services into digital journeys can leverage XAI to reassure customers that they are acting in their best interests – from ensuring affordability and providing transparency of terms and conditions to supporting them if they experience repayment difficulties.
Challenges of implementing XAI in embedded finance
Yes, XAI is a robust, descriptive tool that offers in-depth insights in comparison to opaque AI models – but it has its own sets of challenges:
- Bias: AI systems are only as good as the data they are trained on. Biased data typically leads to prejudice in automated outcomes that can lead to discrimination and unfair treatment.
- Fairness: Its perception of fairness in terms of the decisions it takes is contextual and depends on the information fed to the machine learning algorithms.
- Cost: Implementing XAI-based systems can be expensive, particularly for small and medium-sized businesses that may not have the resources to invest in such technology.
- Data privacy and security: Its ability to collect and process large amounts of data exposes it to privacy concerns. The data may contain personal information which, if not handled properly, can lead to breaches, identity theft, or fraud.
Use cases of XAI in embedded finance compliance
XAI is helping to power the exponential growth of embedded finance: valued at $54.3 billion in 2022, it is forecast to reach $248.4 billion by 2032. A determination among embedded finance providers to take responsibility for the management of regulatory compliance using XAI – notably fraud, money laundering, and risk – is adding a layer of transparency and trust that’s enhancing its appeal:
- Fraud: Explanations of how or why the AI algorithm arrived at a particular conclusion in the fraud detection process can help investigators pinpoint the source and type of fraud.
- Money laundering: XAI is being used to replace legacy rule-based anti-money laundering models with intelligent algorithms and self-learning solutions that uncover suspicious transactions and patterns.
- Risk: Explainability scores provide clarity during the risk assessment process based on characteristics like the complexity of the dataset on which the model was trained.
Best practices for XAI in embedded finance
The process of implementing XAI functionality into an embedded finance solution in an integrated and synchronised manner is sometimes mismanaged, leading to crippling pain points – from misaligned objectives and requirements to a lack of scalability.
Embedded finance providers that adopt a logical step-by-step approach to the implementation process harness the power of XAI to streamline the management of regulatory obligations:
- Business and data understanding: Define the business objectives and translate them to XAI-related goals, collect and verify the data quality, and assess the project feasibility.
- Data preparation: Produce a dataset for the subsequent modelling phase.
- Modelling: Craft one or multiple models that satisfy the constraints and requirements.
- Model evaluation: The performance of the trained model should be validated against a test set.
- Model deployment: This phase is characterised by its practical use in the designated field of application.
- Model monitoring and maintenance: XAI models are typically used over a long period and their lifecycle must be managed. Failure to maintain the model can cause the degradation of performance over time, leading to false outcomes.
The future of XAI in embedded finance
Predicated on the need to build trust in AI models, the global XAI market size is estimated to grow from $3.5 billion in 2020 to $21 billion by 2030 – acting as a strategic differentiator for those that embrace it.
According to the 2022 IBM Institute for Business Value study on AI Ethics in Action, 79% of CEOs are prepared to embed AI ethics into their AI practices, up from 20% in 2018. More than 67% of respondents who value AI ethics indicated that their organisations outperform competitors in sustainability, social responsibility, and diversity and inclusion.
Viewed through the lens of embedded finance compliance, XAI will not only continue to build trust in AI-based solutions; its ability to flag errors will drive improvements in compliance processes.
Against a backdrop of regulatory development in the embedded finance space, there is also scope for fintech providers to establish industry best practices – with XAI at their core – that can help define future data and privacy policies – levelling the playing field for nonbank companies to provide financial services.
XAI: building trust in embedded finance compliance
There’s no questioning the benefits of AI when it comes to conducting real-time analysis of vast datasets – but there’s a stigma attached that it’s found hard to shake: its proliferation as a tool to increase efficiency, save money, and inform decision-making has raised questions about the trustworthiness of the outcomes it produces.
While XAI is not a silver bullet, it is ensuring AI can be better understood by making algorithms and their application less enigmatic. Consequently, humans are inclined to trust the AI model because the characteristics and rationale of the AI output have been explained.
Forward-thinking businesses that embrace embedded finance are benefitting from XAI functionality that’s building trust in compliance management – elevating the integrity of these organisations.
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Understanding the Role of Explainable AI in Embedded Finance Compliance
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