The financial services sector is constantly changing, adapting to new policies, regulatory compliance, and the ongoing digital transformation. Numerous decisions must be taken every day to manage business efficiently, making it extremely complicated to pinpoint the right decisions in a shifting landscape.
Increasingly often, financial institutions are turning to eXplainable AI (XAI) software to help them approach current and future challenges. By providing clear and understandable data insight (for instance via If-Then rules, see here for more details), eXplainable AI can dramatically improve the whole decision-making process, making it more efficient and informed.
But not all digital transformations are the same, and many financial institutions still find it difficult to decide which solution to adopt and how to integrate it within their organization. In other words, eXplainable AI still raises doubts, especially in the financial sector, which is so sensitive to industry-specific regulations and privacy law.
How can eXplainable AI help financial institutions approach the digital transformation in the best possible way?
In our experience, there are 3 key points that make a difference to the digital transformation of financial services.
1.Valuing domain expertise
While accurately applied machine learning algorithms produce important data-driven insight, the value of domain expert know-how and company policies should never be overlooked. Any digital transformation software should integrate both machine outcomes and human understanding.
Domain experts, such as credit specialists and banking professionals, will continue to play a vital role in business decisions. Technology “augments” their know-how, rather than replacing it. Explainable AI plays a fundamental role in this sense, since it provides understandable outputs that can be “integrated” into domain experts’ knowledge.
Relying blindly on a new technology is extremely risky, particularly when dealing with sensitive data. The risk increases when we rely on black box algorithms, i.e., systems producing output that is not interpretable by humans.
There may be biases in input data, for example, gender, racial or ideological biases, as well as incomplete or unrepresentative datasets. The issue is particularly relevant when dealing with sensitive decisions, such as granting loans or detecting cases of fraud.
The transition to data-driven decision-processes should be transparent and human-centric. By providing clear, transparent output, eXplainable AI makes it possible to identify biases in advance and thereby avoid them.
3.Respecting privacy regulations
According to GDPR and other international regulations, algorithms applied to personal information must process data transparently and provide a clear explanation of any predictions made. This is impossible with black box systems, which although they provide explanations on decision-making methods, do not allow us to interpret an individual decision. This is unacceptable in cases where clients are entitled to a proper explanation, such as when they are refused a loan.
By contrast, eXplainable AI guarantees individual explanations by using If-Then rules. While XAI provides explanations, it requires an expert to outline the final decision to the client.
Conclusion – future perspectives for financial institutions
In recent years, the banking and insurance market has witnessed a significant increase in the use of artificial Intelligence to combat fraud, estimate risks, and recover funds.
The increasing use of these disruptive technologies has, however, been accompanied by concerns over sensitive data. Rulex’s eXplainable AI is a practical example of trustworthy, GDPR compliant, human-centric AI, which can be confidently adopted in the financial services field.