CXOToday has engaged in an exclusive interview with Karan Mehta, Founder and CTO, RING
- What does hyper-personalization mean in the fintech context?
Hyper-personalization in the Indian or global fintech context refers to the practice of tailoring financial services and experiences to meet the specific needs, preferences, and goals of individual customers in a targeted and personalized manner. It is about giving each individual a customized experience that is not based on just his demographic attributes, but how he or she behaves as a customer. In recent years, AI and ML technologies have begun being utilized to analyze vast amounts of customer data and derive meaningful insights that have enabled fintech companies to offer highly customized solutions to their users.
- How is AI being used to achieve this? (Technical answers)
- Customer Profiles: Fintech companies employ AI algorithms to analyze a wide range of customer data, including transaction history, spending patterns, and preferences beyond their demographic information. This enables them to develop detailed customer profiles and personas which include customers’ financial behavior, needs and aspirations. Such profiles assist in customizing financial services to meet individual requirements effectively.
- Personalized Product Recommendations: Through the utilization of AI algorithms, fintech platforms can leverage their customer profiles and historical data to offer tailored product recommendations. For instance, AI algorithms take into account factors such as spending habits, travel preferences, and rewards preferences to provide personalized suggestions for credit cards or store offers. This personalized approach enhances the customer experience and facilitates informed decision-making.
- Risk Assessment and Fraud Detection: AI plays a vital role in assessing customer risk profiles and identifying potential fraudulent activities. By analyzing financial transactions and detecting unusual patterns or anomalies, AI algorithms enable fintech platforms to mitigate risks and maintain secure and reliable services for customers. Certain fintech organizations have even developed their own in-house fraud detection software using AI.
- Credit Scoring and Loan Processing: AI algorithms are instrumental in evaluating creditworthiness and streamlining loan processing. By incorporating certain AI techniques and considering various data sources beyond traditional credit history, fintech companies can provide personalized loan offers to individuals who may have been overlooked by conventional banking systems due to limited credit information or collateral availability. This approach promotes financial inclusivity and access to credit.
- Real-Time Insights and Alerts: Fintech platforms leverage AI capabilities to deliver real-time insights and alerts to customers regarding their financial activities. By monitoring transactions and financial behavior, AI algorithms enable platforms to provide timely notifications and alerts. These notifications encompass various aspects such as detecting potential fraudulent activities, notifying about possible overdraft situations, presenting investment opportunities aligned with customer preferences, and delivering personalized budgeting tips based on spending habits. This empowers customers to make well-informed financial decisions and maintain control over their financial well-being.
- Are there regulations in place that companies must follow while using such AI?
The exponential growth of AI has caused much regulatory discussion around the world with Italy having banned Chat GPT out of security concerns, the EU charting out an AI Act, and the US planning for an AI Bill of Rights.
India has been unique in its approach to AI regulations. The Ministry of Electronics and IT (MeitY) stated the following in the Lok Sabha, “The government is not considering bringing a law or regulating the growth of artificial intelligence in the country.” The Indian government intends to position India as a global leader in this field as it sees AI as a ‘kinetic enabler’ and wants to harness its potential for better governance.
MeitY echoed this sentiment in a statement: “The government is harnessing the potential of AI to provide personalized and interactive citizen-centric services through Digital Public Platforms.”
As such, it seems that while fintech services may be regulated, the use of AI within fintech operations will not be for the foreseeable future.
- Have you seen any improvement in user engagement or feedback after implementing such tools?
The implementing hyper-personalization with AI can lead to several expected improvements in user engagement and feedback:
Customer Engagement: AI-powered hyper-personalization captures users’ attention and boosts engagement by delivering customized content. Personalized recommendations, offers, and notifications can encourage users to spend more time interacting with the platform, explore its features, and take actions like making transactions or utilizing specific services.
User Experience- Hyper-personalization improves the user experience by customizing it according to individual preferences, needs, and behaviors. This heightened level of personalization results in a more captivating and pertinent user experience, ultimately boosting customer satisfaction and fostering loyalty.
Customer Retention- Customer retention is facilitated by hyper-personalization, which instills a sense of relevance and value among customers. This increases their likelihood of remaining loyal to the service provider. By comprehending individual customer preferences and behaviors, companies can proactively anticipate their needs and provide personalized solutions. This cultivates enduring relationships and diminishes customer churn, as users perceive a genuine understanding, appreciation, and attentive service.
- What are the challenges and concerns associated with hyper-personalization through AI?
While hyper-personalization of fintech services via AI has many notable advantages for both companies and users, it is important to recognize the limitations and potential dangers of this technology:
Data Security: In order for AI and ML to help with the hyper-personalization of any operation, the company needs to allow the AI access to their entire database of records. In fact, “training” AIs by filtering huge amounts of data through them is the norm in data science. Naturally, such open access to terabytes of personal data means that fintech organizations have to be extremely careful about the safeguards around both the data storage and the AI itself to avoid any malicious attacks or leaks.
Regulatory Compliance: While India has made great strides in encouraging the development of AI and not restricting its usage, the lack of legal clarity can make arbitration of fintech cases involving AI quite difficult for consumers and companies alike. That being said, such incidents can still be addressed by the legal system on a case-by-case basis
Lack of Human Involvement: While removing the fallible human element would seem to be the whole point of AIs, there are many circumstances where people are required to step in. Especially in the fields of finance and credit, where people are so personally involved in the outcome of a loan appraisal or a credit check, the thought of a machine in full control can be unnerving. Fintech companies are quite aware of this, which is why most of them allow for a human to step in at almost every step of the process in case an anomaly is detected or if a customer requests it.
- Do you think such techniques are going to become more prevalent in fintech or is this more of a passing fascination with AI?
AI has been making financial institutions more efficient while helping protect the personal data of millions of users around the world. From storage and sorting ML software to advanced generative AI, the impact of AI on the BFSI sector has been very noteworthy.
A report by AIM Research stated that the Indian AI market size is predicted to grow at a CAGR of 42% in 2022. As AI becomes more powerful and accessible to the public, it is reasonable to expect a continued uptick in the utilization of AI-adjacent techniques like hyper-personalization. While we may eventually reach a plateau in terms of the use cases for AI in fintech, there are still many avenues and technologies to explore in the field of fintech AI and AI as a whole.
