In this insightful interview, we sit down with Srikumar Ramanathan, Chief Solutions Officer at Mphasis, a leading global IT solutions provider. With over three decades of experience, Srikumar has been at the forefront of technology innovation in financial services. From his early days modernizing systems at the Singapore Derivatives Exchange to leading digital transformation efforts at Citibank, Srikumar has consistently leveraged technology to address complex challenges. In our conversation, he shares his perspectives on the evolving role of AI and cloud computing in financial services, fostering a culture of innovation, and advice for aspiring technology leaders.
Could you share some key milestones in your career journey that have significantly shaped your approach to technology and innovation in the financial services sector?
Over the span of my career in applying technology to solve business problems and opportunities, several key milestones have significantly influenced my approach to technology and innovation.
I started my journey as a software engineer and technology consultant. It is one such consulting assignment that exposed me to financial services. I was one of the leads in a challenging project for modernizing the legacy systems at the Singapore Derivatives Exchange. The successful execution of that project led to the opportunity to be the CIO for the Exchange. Early in my tenure as the CIO, two major incidents helped me in shaping my approach to technology. The first was the impact of the Barings Crisis, an incident that happened with a trader in the exchange. The crisis led to a transformation of the systems. Typically, the traders and their staff spend many hours after the trading closed to reconcile, but the system changes that we made meant that they could all leave hours earlier than before. This demonstrated to me the importance and the impact process, and systems changes can have on real people. The second was the introduction of electronic trading, in a traditional open outcry trading floor. This drove home the importance of not just the impact that technology can create but also the need for a comprehensive change management strategy for a successful implementation. This experience taught me the profound impact of technological advancements on market efficiency and customer satisfaction.
My tenure at Citibank as the Regional CIO for consumer banking technology in Asia marked another significant milestone. It was here that I spearheaded numerous digital innovations and led the Core Banking Modernization effort across Asia. This role reinforced the importance of staying ahead of technological trends and continuously seeking innovative solutions to enhance customer experience and operational efficiency.
Joining Mphasis marked a new chapter in my career. Initially, as Senior Vice President, Global Head of Industry Solutions, I focused on building a vertical industry approach, which deepened my understanding of various industry-specific challenges and solutions. Now, as the Chief Solutions Officer, I lead the Portfolio Group, overseeing a team of Industry Specialists, Solution & Enterprise Architects, and Cloud Experts. My primary responsibility is to ensure that our solution offerings are not only the best in class but also innovative and relevant to our customers’ evolving needs.
As Chief Solutions Officer at Mphasis, what strategies do you employ to ensure that your solution offerings remain cutting-edge and relevant to your customers’ evolving needs?
Certainly, as Chief Solutions Officer at Mphasis, my primary focus is to ensure that our solution offerings consistently meet the dynamic needs of our customers while remaining at the cutting edge of technological innovation.
We leverage our unique Front2Back (F2B) approach to deliver significant value to businesses. This involves modernizing the ecosystem by shrinking application cores and integrating a cloud and cognitive layer, which combines AI, analytics, and other intelligence forms. This approach empowers the front end without disrupting the back end, providing a seamless and efficient solution to our clients, especially in the banking sector.
AI was integral to our operations long before it became the industry buzzword. Our early adoption and continuous evolution in AI, including large language models (LLMs), positioned us to effectively leverage generative AI advancements like ChatGPT. By building on our established digital transformation foundations, we can introduce AI innovations in ways that directly address our clients’ needs.
We also prioritize agility, allowing us to respond quickly to changing needs and deliver solutions that evolve with our customers’ requirements.
How do you see the role of AI and automation evolving in the financial services industry, especially in terms of enhancing business operations and customer experiences?
The role of AI and automation in the financial services industry is poised for significant evolution, bringing transformative changes to both business operations and customer experiences. Here’s how I envision this transformation unfolding:
· Enhanced Customer Experiences: The implementation of AI-driven solutions, such as chatbots and virtual assistants, significantly enhances customer interactions. These tools provide instant responses, personalized advice, and round-the-clock support, leading to higher customer satisfaction and engagement. Mphasis.ai, our dedicated generative AI division, is at the forefront of developing and deploying these conversational AI tools, ensuring that our clients can offer superior customer service.
· Predictive Analytics and Personalization: AI’s ability to analyze vast datasets in real time enables financial institutions to offer highly personalized services. Predictive analytics can anticipate customer needs, recommend products, and detect potential issues before they arise. This proactive approach not only improves customer satisfaction but also fosters loyalty and trust. By leveraging our advanced AI models, we help financial institutions create personalized customer journeys that are responsive and intuitive. The same analytics are applied in the backend to enhance the resiliency and availability of key infrastructure that are required to render services. This, in fact, is applicable to all enterprises.
· Risk Management and Security: AI plays a crucial role in enhancing security and managing risks within the financial sector. Advanced AI algorithms can detect fraudulent activities by analyzing patterns and anomalies in transaction data. Leveraging analytics-driven detection and automated response tools combined with our cyber defence expertise, we provide robust threat monitoring and response services. This ensures that financial institutions can protect their assets and customer data more effectively.
· Innovation and Competitive Edge: Embracing AI and automation fosters innovation within the financial services industry. Institutions that leverage these technologies can develop new products and services, enter new markets, and stay ahead of the competition. Our ongoing partnerships with leading Hyperscalers and specialized AI platforms ensure that we remain at the cutting edge of AI innovation, providing our clients with the latest and most effective solutions.
In your previous role at Citibank, you initiated several digital innovations. Can you highlight one innovation that had a significant impact on the bank’s operations and customer service?
Citibank, especially in Asia, was seen as an innovative consumer bank and we were operating in multiple countries in Asia. Being a large organization we had to adapt to the cultural and regulatory differences in countries, which meant that while the core functions that we provided were more or less common, they had to be modified to suit the diversity of customer needs and regulatory requirements.
One of the biggest challenges at that juncture was to balance our need to be agile and our need for customization. For example, if we introduced a product or service innovation in one country and it proved to be very successful, it took us a long time to replicate this in other dozen or so countries in the region. Our regional CEO posed us with the challenge of dramatically reducing this time, without compromising on the country-specific customization requirements. I along with my team, with support from the business leaders successfully achieved this.
The other interesting initiative was the whole digital banking revolution that was taking place. The building of the digital platform, which was resilient and secure but at the same time very flexible and agile was very important for the business.
This platform revolutionized the way customers interacted with the bank, offering a seamless and intuitive online and mobile banking experience. Customers could perform a wide range of banking transactions, from checking their account balances and transferring funds to paying bills and managing investments, all from the convenience of their smartphones or computers. Of course, today, all these are taken for granted.
Not only did this digital banking platform enhance customer satisfaction by providing greater convenience and accessibility, but it also significantly streamlined Citibank’s operations. With more customers shifting to digital channels for their banking needs, there was a reduction in foot traffic at physical branches, leading to cost savings and improved operational efficiency.
Moreover, the platform’s robust security features ensured that customers’ sensitive financial information remained protected, building trust and confidence in Citibank’s digital offerings.
Overall, the implementation of this digital banking platform represented a pivotal moment in Citibank’s journey toward digital transformation, demonstrating its commitment to leveraging technology to enhance both operational excellence and customer service.
Could you elaborate on how AI democratizes data and the implications this has for analysts and business decision-making processes?
AI democratizes data by making advanced analytics and insights accessible to a broader range of users within an organization, beyond just data scientists and IT professionals. This democratization has profound implications for analysts and business decision-making processes. Here’s how:
· Accessibility and Usability: AI democratization simplifies complex data analytics, making it more accessible to business users across various departments. With intuitive AI tools and interfaces, employees can now perform sophisticated analyses without needing deep technical expertise. At Mphasis, we integrate AI capabilities into our existing technology landscape through our Mphasis.ai unit, ensuring that AI tools are user-friendly and accessible to all.
· Scalability and Agility: Cloud integration plays a pivotal role in AI democratization by providing scalable and flexible infrastructure to support AI applications. Our comprehensive cloud adoption at Mphasis, which allows us to leverage the latest technologies seamlessly, ensures that our AI initiatives can scale rapidly to meet growing demands. This agility is crucial for staying competitive in the dynamic financial services industry.
· Cost Optimization and Resource Allocation: Automation reduces operational costs and frees up resources for strategic initiatives. By leveraging open-source AI models and selecting the most appropriate solutions for specific tasks, we ensure cost-effectiveness without compromising on quality or effectiveness.
· Innovation and Future Growth: AI accelerates innovation and drives future growth by enabling financial institutions to develop new products, enter new markets, and stay ahead of the competition. Our focus on establishing a path of AI acceleration, combined with our cloud-first approach and strategic partnerships, positions us for continued success and leadership in the evolving landscape of financial services.
How do you foster a culture of innovation and continuous improvement within your team at Mphasis?
At Mphasis, fostering a culture of innovation and continuous improvement is a deeply personal commitment that involves several targeted strategies. One of our early recognitions of the need to collaborate across organizational structures to foster true problem-solving. We achieved that by adopting and adapting the Tribes and Squad model introduced by Spotify. This helped us to bring the key members with diverse skills in loosely coupled but highly focused teams to innovate in specific domains.
Besides this, we also realized that technology is moving so fast that we need dedicated resources looking around the corner to anticipate and experiment with newer inventions. Our NEXT Labs, established in 2015, serve as the nucleus of our research and innovation efforts. These labs focus on emergent technologies and future paradigms, allowing us to create industry-relevant solutions and disruptive innovations.
For example, our innovation labs have developed platforms like DeepInsights™, a patented cognitive intelligence platform that has transformed decision-making processes for our clients by providing faster and more effective data insights. Another notable innovation is our work on quantum computing with Mphasis EON, which is pioneering new approaches to machine learning and optimization problems.
Collaboration is key to our innovation strategy. Through NEXT Labs, we work with internal stakeholders, academia, start-ups, and global technology partners. This collaboration has led to innovations like HyperGraf™, a business intelligence solution. Additionally, Mphasis.AI is our commitment to AI-driven transformation. We create personalized interfaces, enable intelligent decisions, and implement process automation. Our Gen AI Center of Excellence delivers customized outcomes, such as enhanced market share through contact center transformation solutions leveraging Kore.ai’s technology and Blink’s design.
What are some of the key considerations when integrating AI and ML technologies into existing financial systems to ensure a smooth and effective transition?
In my experience, successful integration of AI and ML technologies into existing financial systems requires careful consideration of several factors. The democratization of AI has allowed people to innovate and identify use cases for applying AI to their day-to-day work. There is a proliferation of proof of concepts but taking these ‘POCs’ to production requires careful planning. We are doing a lot of work in this area to help our customers harness innovative ideas and take them to fruition.
Integrating AI and ML technologies into existing financial systems requires careful planning and consideration to ensure a smooth and effective transition. Here are some key considerations:
- Data Quality and Availability: Ensure that the data available for training AI/ML models is of high quality, relevant, and readily accessible. Data cleansing and preprocessing may be necessary to address any inconsistencies or inaccuracies in the data.
- Regulatory Compliance: Compliance with regulatory requirements, such as GDPR or financial regulations like Basel III, is paramount. Ensure that AI/ML implementations adhere to relevant laws and regulations governing data privacy, security, and ethical use of AI.
- Model Interpretability and Transparency: AI/ML models used in financial systems must be interpretable and transparent to stakeholders, including regulators and customers. Understanding how the model arrives at its decisions is critical for building trust and ensuring accountability.
- Robustness and Security: Implement measures to safeguard AI/ML models against adversarial attacks, data breaches, and cyber threats. Ensure that robust security protocols are in place to protect sensitive financial data and prevent unauthorized access.
- Scalability and Performance: Consider the scalability and performance requirements of AI/ML solutions to accommodate growing volumes of data and user interactions. Ensure that the infrastructure supporting AI/ML deployments can handle increased workloads without compromising performance.
- Human Oversight and Governance: Maintain human oversight and governance over AI/ML systems to monitor their behavior, detect anomalies, and intervene when necessary. Establish clear policies and procedures for model governance, risk management, and compliance.
- Ethical and Fair Use: Address concerns related to bias, fairness, and ethical use of AI/ML technologies in financial systems. Implement mechanisms to identify and mitigate biases in data and algorithms to ensure fair treatment of customers and prevent discriminatory outcomes.
- Change Management and Training: Provide adequate training and support to employees to facilitate the adoption of AI/ML technologies. Address any concerns or resistance to change through effective change management strategies and communication.
- Integration with Existing Systems: Ensure seamless integration of AI/ML technologies with existing financial systems, such as core banking platforms, CRM systems, and risk management tools. Compatibility and interoperability with legacy systems are essential for minimizing disruption and maximizing ROI.
- Continuous Monitoring and Optimization: Establish mechanisms for continuous monitoring and optimization of AI/ML models to ensure their effectiveness and relevance over time. Regular performance evaluations and model retraining are essential for maintaining accuracy and reliability.
In what ways do you believe cloud computing will continue to transform the financial services industry, and what should organizations do to stay ahead of this curve?
I’ve witnessed the profound impact of cloud computing on businesses, particularly in navigating the complexities of integrating cloud and Software-as-a-Service (SaaS) offerings. Over the years, many of our clients have successfully overcome initial adoption challenges, only to face new hurdles in creating integrated IT operations and security models across diverse deployment environments.
Looking ahead, I foresee a greater emphasis on leveraging AI and automation to drive consistency and seamlessness in cloud, SaaS, and on-premises deployments. This pursuit of consistency is essential as cloud computing evolves from being an IT “target” to a facilitator of key business initiatives. Our goal is to shift focus from specific technologies to defining essential capabilities and constraints, enabling organizations to seamlessly integrate market options while ensuring stable, cost-effective operations and comprehensive reporting.
In the financial services industry, cloud computing continues to revolutionize operations, offering unparalleled opportunities for innovation and efficiency. It enhances scalability, flexibility, and cost-efficiency, empowering institutions to optimize operations and rapidly deploy new services to meet evolving customer needs. Furthermore, cloud-based AI and analytics are transforming functions from risk management to customer engagement, extracting insights from vast datasets.
To stay ahead in this transformative landscape, organizations must adopt a cloud-first strategy, prioritize cybersecurity, and embrace continuous innovation. Strategic partnerships with leading cloud providers further enhance capabilities and drive sustainable growth in the cloud era. By embracing these principles of consistency, innovation, and strategic alignment, organizations can thrive amidst the ongoing evolution of cloud computing in the financial services industry.
Can you share an example of how a specific AI or ML solution developed by your team has directly contributed to improved business outcomes for a client?
Certainly. One notable example is a predictive analytics solution we developed for a prominent financial institution to revolutionize their credit risk assessment process. Leveraging advanced machine learning algorithms, our team analyzed vast datasets encompassing diverse customer profiles, transaction histories, and market trends.
This initiative aligns with our vision at Mphasis.AI to transform businesses with the power of AI. By creating composable experiences using AI, we developed a hyper-personalized solution tailored to the client’s specific needs.
The outcome was transformative. By accurately predicting the likelihood of default, our solution empowered the client to make informed lending decisions, significantly reducing the incidence of bad loans. This, in turn, bolstered the quality of the bank’s credit portfolio, mitigating risks and enhancing financial stability.
Moreover, our AI-driven approach led to substantial cost savings for the client. By minimizing the occurrence of defaults and associated losses, the financial institution realized notable efficiencies in its operations, translating into tangible financial gains.
What advice would you give to aspiring technology leaders looking to make a significant impact in the financial services sector?
My advice to aspiring technology leaders is to stay curious and continuously learn not just about emerging technologies and trends but also about the business. Building a strong foundation in both technology and business is crucial to understanding and addressing the unique challenges of the financial services sector. Focus on developing problem-solving skills and a customer-centric approach. Networking with industry professionals and seeking mentorship can provide valuable insights and guidance. Lastly, be adaptable and open to change, as the financial services landscape is continuously evolving. Leaders also have to pay a lot of attention to the people and process aspects, both from getting the right teams to work together and also taking care of the change management required to ensure smooth adoption. Tracking the realization of the benefits envisaged while designing the system is also crucial.
