Overcome the 5 Critical Challenges of AI in Fintech

As AI applications become more prevalent in financial services, they have also brought forward questions around data security and transparency.

Data management practices are also evolving with the introduction of new AI solutions. Hence, it will be important for financial services firms to be aware of the expected challenges of implementing AI and build necessary safeguards.

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Challenges of Implementing AI in Financial Institutions

According to Forbes, “70% of all financial services firms are using machine learning to predict cash flow events, fine-tune credit scores and detect fraud.” But despite this upward trend, many are struggling to implement AI due to reasons such as:

Poor Data

Poor data quality is the top barrier to adoption in financial services and the main reason why AI applications have failed to deliver expected results.

There is no dearth of data with financial organisations, but much of it is irrelevant and of poor quality, leaving businesses with redundancies and inconsistencies. Finding high-quality data will need companies to pay more attention to cleansing, labeling and warehousing, along with workflow changes and better cataloguing.

Biased Data and AI Ethics 

Decisions made by AI can have a significant impact on the customers of financial institutions. Working with biased data sets can enforce prejudices in society and even lend scientific credence to them. Biased outputs from AI algorithms stemming from skewed data have stilted decision-making AI applications.

As financial institutions train their AI models, they have to be aware of and eradicate bias, specifically programming AI to be anti-bias.

Humans must still be in the decision-making loop, and training data must be analysed before utilising it for AI algorithms.

Data Governance

In the face of rising cyber crimes and consumer concerns, governance has emerged as a challenge in AI. Even the highly regulated financial services industries have failed to ensure proper governance around customer data.

Companies can embrace edge computing and data segmentation, and ensure data transparency to minimise the effects of a data breach and gain back some of the trust they lost with their consumers.

Black Box Problem

AI algorithms that aren’t explainable can lead to more bias and have significant consequences. The financial industry is heavily regulated, and any decision made by AI algorithms must be fully understood by the institution. AI model explainability is crucial in the financial industry.

The field of explainable AI is developing, and researchers are working on creating model-agnostic approaches that allow explaining the decisions of both relatively simple and very complex models, such as the Local Interpretable Model-agnostic Explanations.

Regulatory Compliance

AI and data-centric operations are facing more regulations. In 2020, across the U.S., 38 states introduced new cyber security legislation. Financial institutions have become more AI and data-driven, as they have to keep compliant with new regulations.

Since the field of data regulation is relatively new, rules are likely to evolve over the next few years; hence, it’s even more important for financial organisations to remain flexible and adopt high privacy and governance standards even before they are regulated.

As the financial industry moves gradually towards AI-driven automation, organisations will need substantial amounts of reliable training data to get their AI projects into the real world.

Finance managers and executives need to know how data can be handled securely and with transparency to positively affect the outcomes of their AI projects.

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