"The development of full artificial intelligence could spell the end of the human race. It would take off on its own, and re-design itself at an ever-increasing rate."
These words spoken by renowned physicist Stephen Hawking in 2014 hinted at the transformative potential of AI, sparking both excitement and concerns. Fast forward to November 2022, ChatGPT burst onto the scene, showcasing its versatile abilities to create content spanning from poems to code.
This generative AI stirred debates about the possible threat it posed to human roles in professional settings, such as the Finance ecosystem.
However, Rangarajan Vasudevan, Chief Data Officer at Lentra, urges a balanced perspective, emphasizing that AI's advancements in Finance are an ongoing journey, and fears of human obsolescence are premature.
Let’s delve into his insights about the nuances of AI, exploring its inherent limitations, especially in the lending sector.
Navigating AI’s Landscape: Unraveling the Challenge of Context
While AI has automated numerous finance jobs, including white-collar roles like Financial Advisory and Analysis, it still grapples with the essence of human understanding, empathy, and contextual comprehension.
The challenge lies in AI's ability to comprehend and generalize from the vast array of human experiences, a realm where human aptitude still holds an edge.
AI's limitations in capturing context make it premature to label it superior to human intelligence.
Despite AI's current limitations in understanding human context, AI models prove adept at analyzing vast amounts of data within the domain of Data Science.
This ability makes AI excel in precise analysis of borrower behaviours, credit histories, and financial patterns, leveraging comprehensive data analytics and predictive modeling.
Unveiling AI's Potential: A Balancing Act of Creativity and Consistency
Although creative and prompt, AI falls short in consistency when compared to humans.
The learning process of AI is often unpredictable, lacking the consistency that human learning achieves through feedback and experience.
The black-box nature of AI models raises concerns about their reliability and consistent performance over time, emphasizing the need for human guidance and intervention.
However, AI helps to identify transaction patterns and predict creditworthiness with a precision that often surpasses traditional methods.
AI's efficient data processing and actionable insights, driven by its automation capabilities, empower lenders to improve risk assessment, make informed lending decisions, optimize loan approvals, and provide customers with more personalized offerings.
AI vs Developers:
The ability of AI to generate code has intrigued many, raising questions about the role of developers, particularly those at entry levels.
However, the quality and reliability of AI-generated code vary, showcasing the need for human developers who possess the understanding and skill to rectify and enhance AI-generated solutions.
The symbiosis of AI and human expertise is crucial for optimal outcomes.
AI streamlines code generation in the lending landscape, enhancing efficiency and speed for digital lending platforms.
It enables rapid prototyping and customization, crucial for staying agile in the competitive financial technology sector.
Transparency and Data Privacy:
A growing concern with AI is data privacy.
AI is trained on vast amounts of data from the internet, making privacy a critical consideration.
Sharing sensitive information with AI raises questions about data privacy and the potential dissemination of confidential data.
It underscores the importance of responsible data handling and privacy protocols in AI utilization.
Within an API-first lending cloud platform like Lentra, emphasis is placed on data security, strictly following an Information Security policy that guides the handling of data across the platform.
The data is end-to-end encrypted, especially while fueling the AI/ML algorithms.
Leveraging the Benefits of AI:
While public Large Language Models(LLMs) may have some limitations, native AI engines, like those of Lentra AI, showcase immense potential, especially in the lending sector.
A significant challenge arises from the lack of comprehensive analytics and tailored scorecards for loan origination and underwriting, particularly for diverse individuals and businesses across different regions of India.
For example, a farmer in the sugarcane belt of UP should not be assessed using the same credit model as a banker in Mumbai.
AI can bridge this gap by personalizing credit decisioning.
Currently, many lenders rely on limited data from traditional credit bureaus and isolated in-house data.
Lentra brings a different approach — a SaaS-based data and insights platform that allows lenders to easily access advanced analytics for seamless lead acquisition, loan origination, and collection processes.
Thanks to the AI and ML algorithms integrated by Lentra.
Lending is rapidly evolving through an API-driven modular architecture that helps in analyzing thousands of data points from diverse sources to trace individual and SMEs’ digital financial footprints.
The outcomes are exceptional—achieving a staggering 90+% Straight Through Processing (STP) rate.
This approach broadens access to a new customer base, reduces customer acquisition costs, minimizes Non-Performing Assets (NPAs), and enhances operational efficiencies.
Moreover, these advantages are seamlessly translated to borrowers, promoting financial inclusion, expediting loan approvals, and ensuring a smooth digital lending experience.
The journey of AI in finance is that of nurturing a harmonious synergy between AI capabilities and human expertise.
It's about leveraging AI for its benefits while being mindful of its current limitations.
In its evolving state, AI is a partner to humans rather than a replacement, as emphasized by IBM Chairman & CEO, Arvind Krishna, in a recent CNBC interview.
With continued research and responsible application, we can unlock new opportunities with AI while preserving the irreplaceable human touch.
As finance companies increasingly invest in training programs and forge partnerships with universities to bridge the AI skills gap, it becomes imperative for finance professionals to keep pace with the rapid advancements in AI.
They must cultivate AI skills to effectively leverage data and AI-driven solutions for making informed financial decisions.