By Stuart Tarmy
In today’s dynamic financial landscape, technology has become the cornerstone of innovation. Artificial Intelligence (AI) is not just a buzzword, it’s a transformative force, disrupting and redefining traditional financial services. As we delve into the intricate workings of this transformation, we discover the profound impact of AI, the emerging interest in explainable AI and the effect that real-time data is having on financial institutions.
What is Explainable AI and How is it Shaping Financial Services?
Transparency is vital in financial services, where AI decisions must be understood and justified. Explainable AI (XAI) helps organizations ensure that AI decisions are understandable and traceable, essentially allowing them to see what’s happening inside the “black box.” It allows companies to provide faster, reasonable explanations to users, auditors and regulators to show that the system is not producing biased or discriminatory output.
According to a PwC-FICCI report, 83% of Indian FS organizations have identified enhancing customer experience as the top driver for AI-enabled use cases, and 57% strongly agree that AI will give them a competitive edge over their peers. XAI empowers financial sector companies to improve their AI models by providing insights into their inner workings, so they can fine-tune business-critical systems for things like customization and personalization.
But the move towards AI isn’t just about improving customer experiences; it also addresses the pressing need for financial institutions to adapt their AI models to effectively address certain events and long-tail scenarios, such as unanticipated shifts in interest rates, customer behavior or global macroeconomic events. By doing so, they not only bolster their adaptability to unexpected situations but also increase the likelihood that they’ll be in sync with regulatory expectations, all while harnessing the power of AI to stay ahead in the fiercely competitive financial landscape.
The rising demand for transparency in AI’s decision-making processes amongst auditors, regulators and various stakeholders is another critical factor in the rise of XAI. The increasing use of it in financial services resonates with Prime Minister Narendra Modi’s call for a global framework for the expansion of “ethical” AI tools. These frameworks address the expectations of regulatory bodies and industry stakeholders, promoting trust, integrity and innovation within the sector. Other notable global AI transparency initiatives include the USA’s National AI Initiative Act of 2020, EU AI Act and the UK National AI Strategy.
Integration of Explainable AI and Real-time Data
With the shift from traditional to modern FSI architectures, it’s important for the sector to embrace the multi-dimensional role of AI and understand the importance of integrating real-time data and transparency to fully realize AI’s benefits.
AI needs massive amounts of real-time data along with a robust infrastructure that ensures consistent performance at scale. The fusion of XAI and real-time data allows organizations to constantly improve their systems. It also allows them to accelerate customer onboarding without compromising compliance and speed fraud mitigation.
XAI combined with real-time data analysis provides the vital capabilities of monitoring and understanding system responses and outputs in light of rapidly changing market conditions. It also establishes a continuous feedback mechanism that equips financial institutions with the technology they need to proactively detect and manage emerging risks, ensuring they remain adaptable and competitive.
By embracing XAI and real-time data, financial sector companies can bridge technological complexity and practical use, thereby closing the gap between data scientists (who develop and fine-tune these algorithms) and business executives, who benefit from the ability to make better, more accurate decisions with the aid of AI, XAI and real-time data.
Conclusion
AI is shaping the power dynamic of the financial services landscape, but the predictions that AI systems make need to be fully transparent and based on accurate data so that it can be trusted. Explainable AI combined with a real time data infrastructure that offers predictable performance, massive, unlimited scale and strong consistency will give financial services organizations the advantages they need to better serve their customers and win against the competition.
The author is head of industry vertical solutions, fintech and global partnerships, Aerospike
