Murari Lal, Head – Digital Initiatives, Shivalik Small Finance Bank, delves into platforms, loyalty and AI:
Smriti Pandey: The strategy at many financial organizations is shifting towards platform adoption. Briefly describe 2 upsides and 2 downsides associated with platforms that you have experienced in the recent past.
Murari Lal: It is true that financial organizations are increasingly shifting their approach towards adopting platform as part of their digital transformation strategies. Today platforms are capable of enabling banks to integrate quickly on various services, streamline operations, and offer a more seamless and personalized customer experience. By adopting platforms, financial institutions can partner with third-party service providers, fintechs, and bigtechs to innovate faster, scaling their offerings in areas like digital payments, digital onboarding, lending, and wealth management.
This shift is driven by the need for quick go to market, cost efficiency, enhanced customer engagement, and the ability to use data more effectively for personalized services. Additionally, platforms allow for faster onboarding of new technologies like AI, which are transforming how financial services are delivered today.
However, this shift comes with its challenges too. Banks must navigate to the cybersecurity risks, complex regulatory environments, and the integration of legacy systems. Despite these challenges, the platform approach is positioning financial organizations to stay competitive in a rapidly evolving market, making it a key focus for the future of finance.
Upsides of platform adoption for financial organizations:
Scalability & better customer experience: On one hand, platforms allow financial organizations to reach a broader audience and scale their services quickly by leveraging technology, partnerships, and third-party integrations. And on the other hand, by offering an engaging customer flows, unified platform, and by providing seamless and personalized services, such as integrated banking, lending, and investment options add up to better an enhanced customer experience.
Enhanced Financial Inclusion: Banking platforms allow banks to offer services digitally, reaching underserved or unbanked populations in remote areas via mobile apps or digital banking services. Downsides of platform adoption:
Cybersecurity Risks: As in platform approach where everything is digital, they become attractive targets for cyberattacks, raising concerns about data breaches, fraud, and compliance with regulatory standards if not implemented or managed well.
Dependency on Third Parties: In a platform approach, the bank does not own everything and relies on third-party providers for few critical services. This introduces risks related to partner performance, control over customer interactions, and potential regulatory compliance challenges, if partners fail to meet standards.
Customer loyalty is diminishing in the digital ecosystem. There were many non-product drivers of loyalty in the physical world, such as branch location, friendly and helpful staff, personal rapport with customers, branch decoration during festivals, home visits, etc. What do you see as the key non-product drivers of customer loyalty in the digital ecosystem. Give 1-2 examples of what you are driving at your organization.
In the digital ecosystem, customer loyalty is built less by physical interactions and more by experiences, trust, and emotional connections that can be cultivated online by many ways. Non-product drivers of loyalty in this space could revolve around factors like ease of use, personalization, and consistent engagement. Here are key drivers:
Seamless User Experience: The ease with which customers can access services, complete transactions, and navigate platforms is critical. If the platform is intuitive, user-friendly, and offers quick resolutions, customers are more likely to stay loyal. A seamless experience also reduces frustration, which often will drive customers away from your digital services.
Data-Driven Personalization: In the digital world, personal rapport is replaced by tailored offerings based on customer data. By understanding customer behaviors, preferences, and needs, organizations can provide personalized financial products, relevant content, and targeted recommendations.
Trust & Security: Digital trust is paramount. Customers need to feel that their personal and financial data is secure. Transparent communication regarding security measures and a track record of keeping data safe can greatly enhance loyalty in the digital ecosystem.
Proactive Customer Support: In the digital ecosystem, where customers may not interact with branch staff, the quality of online support becomes a major loyalty driver. Providing efficient, proactive, and easily accessible customer support across channels – live chat, social media, or in-app assistance – ensures that customers feel supported even without physical interaction.
Each of these strategies builds emotional and practical engagement in the absence of physical interactions, ensuring customers feel understood, valued, and secure.
An AI system can be designed to maximize intellectual capabilities and also to have a balanced humane perspective. What has been your experience in setting both these goals for AI/ML systems and training them? Briefly describe some lessons learnt.
Designing AI systems to balance high intellectual capabilities with a humane perspective has been an evolving challenge in AI/ML development. In my understanding, ensuring this balance requires careful attention to both technical and ethical considerations, along with the integration of human-centric design principles is a need of the hour. Some of the points that we should always keep in mind or lessons that we should learn are:
The Importance of Ethical Frameworks: When developing AI systems, it’s essential to embed ethical considerations from the outset. Intellectual capability, such as accuracy and decision-making efficiency, often conflicts with humane outcomes like fairness, transparency, and inclusivity. By implementing ethical frameworks – such as fairness in algorithms, bias reduction, and privacy safeguards – you ensure that the AI system does not sacrifice ethical considerations for performance.
Human-in-the-Loop Approach: AI systems with intellectual capabilities may sometimes make decisions that are hard to interpret or too rigid in edge cases. Incorporating a human-in-the-loop approach ensures that when the AI reaches its limitations, human oversight can guide it. This leads to more empathetic outcomes, where decisions are more balanced between logic and human understanding.
Continuous Learning and Feedback Loops: AI systems trained with static data might become outdated or fail to generalize in real-world scenarios, especially when handling diverse or evolving human behaviors. Implementing continuous learning models, which can adapt based on user feedback, helps maintain relevance and humane outcomes in a dynamic environment.
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