Just as Salesforce has been instrumental in aligning the technological needs of less agile industries like Higher Education and Housing & Care, Artificial Intelligence is poised to redefine the Fintech landscape. In this article, we delve into the transformative power of two main AI categories affecting the Fintech industry: Generative AI and Machine Learning.
Generative AI
Marketing teams in the financial sector leverage Generative AI to predict market trends. By extrapolating from existing data, these tools provide insights that enable financial firms to make data-backed decisions and what, how and who they market to. This has implications for resource allocation, market entry, and customer engagement strategies.
In addition, Generative AI facilitates the generation of synthetic financial data that is invaluable for stress-testing risk models and various market conditions. This provides Fintech companies with a sandbox environment to better prepare for real-world scenarios and volatile market conditions.
On the customer service front, Generative AI is advancing well-set and understood tools like chatbots and virtual assistants. These tools have long been capable of basic inquiries and tasks, but now Generative AI has the potential to go further by creating more nuanced, context-aware interactions. It could, for instance, generate personalised top-level financial advice or market predictions for customers, elevating the level of personalised service offered and enticing them to drill down further with a representative or self-service sign-up.
Of course, as Generative AI gains traction, it’s vital to discuss the ethical considerations involved. LLMs (Larger Language Models) and Generative AI in general draw information from multiple sources, analyse language patterns and replicate. This can lead to using inaccurate misinformation or even creating false information that just sounds similar to other sources and could promote or create misinformation among users. How these technologies are regulated will likely become a point of contention as they become more prevalent in the financial industry.
Machine Learning
Machine learning algorithms have dramatically improved risk assessment practices, particularly in the realms of credit scoring and fraud detection. They analyse complex datasets at speeds unimaginable to humans, making the risk assessment process more efficient and comprehensive.
However, it’s crucial to note that these algorithms can sometimes perpetuate existing societal biases found in the training data. Ensuring ethical AI usage means continuously vetting these algorithms for bias. Fintech companies can demonstrate their commitment to ethical AI by being transparent about their methodologies and making ongoing adjustments to reduce algorithmic bias.
Algorithmic trading is another area where Machine Learning shines. Predictive analytics and high-frequency trading algorithms are already in place in many institutions, making the trading process more efficient and potentially more lucrative. These algorithms can analyse market conditions in real time and execute trades at speeds far beyond human capabilities.
Current Examples
Google’s Performance Max is now being used across industries, including Fintech, to finetune advertising performance. Performance Max uses AI to optimise ad placements across various channels, thereby providing an unparalleled level of targeted advertising and robust ROI measurement capabilities.
Companies like Enova use machine learning algorithms to offer advanced financial analytics and more reliable credit assessments. Similarly, Ocrolus leverages machine learning combined with human verification to scrutinise financial documents, thereby streamlining loan eligibility processes.
IBM Cloud for Financial Services focuses on mitigating risk and accelerating cloud adoption, with built-in security measures and controls tailored for the financial industry.
Preparing for Future Disruptions
As AI technology evolves, financial services should prepare for potential disruptions and innovations. With developments like quantum computing and increasingly advanced neural networks on the horizon, the Fintech industry must remain agile and forward-thinking to adapt to these inevitable changes.
As we forge ahead into an increasingly digital future, the relationship between AI and Fintech only seems set to strengthen. Generative AI and Machine Learning offer Fintech the tools to overcome existing challenges and streamline operations, all while offering a higher level of service to customers.
