It's been a couple of years now since financial services AI has become mainstream, and institutions all over the world have begun to explore the numerous opportunities artificial intelligence has to offer. But now that the initial excitement has subsided, the global spotlight is increasingly shining on the ethics and legality of how AI is used.
While AI is full of potential for innovation, efficiency, and customer service transformation, it also comes with compliance, ethics, and risk management challenges. These complexities are magnified in a highly regulated sector like banking, financial services and insurance (BFSI), where the consequences of non-compliance can be particularly severe.
In this blog, we'll explore responsible AI in the BFSI sector: why it's important, how it works in practice, and how to strike a balance between ethical usage and driving innovation.
Every business already understands that failing to properly invest in AI means they risk falling behind their competitors. McKinsey has found that 92% of companies plan to increase their AI investments over the next three years.
However, the same should apply to investing in responsible AI frameworks, in order to avoid three major risks that can have substantial business impacts:
Customers expect to be able to trust banks and finance firms, not only with their funds and financial affairs, but also in how their personal data is used and stored. They also expect these businesses to avoid introducing biases and inequalities through AI insights, so that everyone is treated fairly and without discrimination.
As such, any errors or problems tend to attract substantial public scrutiny, and can heavily impact trust and brand reputation. Take the Air Canada chatbot that was promising customers refunds on flights, for example.
The incident involved customers deliberately attempting to manipulate the chatbot's responses to secure flight refunds. Air Canada firmly maintained that the chatbot had provided incorrect information and refused to honor these refund requests. However, when customers took legal action, the airline chose to contest the case in court rather than settle—a decision that ultimately resulted in Air Canada losing the lawsuit.
The risk of regulatory violations, and substantial fines and penalties, is significant for financial institutions that don't have proper AI governance in place.
When financial institutions are found in breach of regulations, they suffer significant reputational damage that erodes customer trust and confidence. Additionally, if compliance issues stem from historical events that weren't properly monitored or documented, organizations must invest substantial resources in forensic investigation and remediation. These operational costs often far exceed the direct financial penalties, diverting critical resources that could otherwise be allocated to improving systems and enhancing services.
The level of investment required for quality AI applications in the BFSI sector can be substantial, which means it's important to get those decisions right and realize maximum ROI. If the deployments fail to deliver value, don't meet governance requirements or don't promote responsible AI usage, then it can cost a lot more to remediating these problems or sourcing a new solution entirely. Organizations that neglect to implement best practices and proper governance frameworks often find their AI investments underperforming significantly. In the worst cases, these investments may deliver virtually no business value whatsoever, resulting in wasted resources and missed opportunities for innovation and competitive advantage.
Responsible AI is now a strategic imperative for any financial institution handling sensitive customer data and making critical decisions. Based on Ciklum’s extensive experience with implementing financial services AI, we’ve identified the six keys to building a structured, innovative, and responsible AI deployment:
Financial institutions must be able to understand and articulate how their AI systems arrive at decisions. You're never going to be able to look into all the workings of a large language model. But you have to be able to at some level understand what your model is doing. Testing the workforce to measure their understanding can be a more practical way of achieving this.
It’s essential to ensure that your data is in good shape, with good management and governance processes to keep quality and standardization high. This governance must cover both training and operational data that feeds into AI systems, factoring in currency, completeness, classification, auditability and versioning, and ethical data sourcing and usage.
You need to be able to monitor these systems as they're operating, and moderate what the models are saying and doing at any given point. This is especially important for incident response. You also need really strong MLOps, DevOps and DataOps capabilities in the background so that you can fix problems as rapidly as possible.
Putting policies in place that align with the strategic thinking around responsible AI will improve regulatory compliance. Using responsible AI as a guideline to how you set up your governance strategies will help with your regulatory compliance now and in the future, including with regional regulations like the European Union’s AI Act.
It’s important to make sure that employees all understand exactly what they're working with, how the technology works, how to interact with it in the best way, and how to accept and interpret output from the AI. Employees working directly with AI systems or interpreting their outputs should receive mandatory training to facilitate this.
If you find bias in the systems, transparency and explainability can help you see what’s happened, why it’s happened and what to do about it. If your model's a big black box and you're getting really biased results, it's a lot harder and more expensive to fix that problem. In financial services, where decisions can significantly impact individuals' financial wellbeing, ethics aren’t optional — they’re essential.
Contrary to popular belief, investing in responsible AI frameworks doesn’t have to inhibit innovation or slow down benefit realization. Indeed, having proper governance in place can actually help AI performance and adoption rather than hinder it.
The best way forward is to strike a balance between innovation and governance, through parallel roadmaps that align with each other, and allow new innovations to be implemented with good governance and responsible AI baked in from the outset.
This strategic approach ensures that governance frameworks enhance rather than impede an organization's ability to remain at the forefront of the AI revolution. By developing governance protocols in parallel with innovation initiatives, financial institutions can prepare new AI systems for immediate deployment once the governance framework is operational. When properly synchronized, the innovation work is already completed and ready for implementation at the precise moment governance protocols are activated. This allows innovative AI systems to move directly into production and adoption without delays, maximizing both compliance and competitive advantage.
Only 1% of businesses feel they have reached maturity with their AI investments, meaning there is still scope for responsible AI to be integrated more closely with future developments. Financial institutions that embrace responsible AI practices now will position themselves for long-term competitive advantage, enhancing customer trust and delivering innovation and efficiency through transparency, data management, operational capability, compliance, AI literacy and ethics.
An expert partner like Ciklum can help you align your innovation and governance roadmaps, and ensure your long-term use of AI meets regulatory and public expectations of responsible AI. Our Experience Engineering approach ensures that your AI implementation not only meets governance requirements but also delivers exceptional user experiences.
Explore our data and AI services today to find out more.