Jay Venkateswaran, Business Unit Head for Banking and Financial Services, oversees strategy, growth, and financial performance. Prior to joining WNS, he was a Consulting Partner at AT Kearney. As a trusted Business Transformation and Services partner, WNS uniquely blends domain expertise with a unified digital ecosystem that integrates Analytics, Hyperautomation, and AI to create market differentiation for clients.
Generative AI is rapidly transforming banking and financial services. How do you see its impact evolving over the next five years?
In banking, I see Generative AI (Gen AI) play a pivotal role in enhancing customer experience, improving operational efficiency, and strengthening risk management. In the back office, AI-driven automation will streamline tasks like regulatory reporting, compliance checks, and document processing, significantly reducing operating costs. Moreover, AI will refine credit risk assessments and fraud detection through real-time data insights and predictive analytics.
One of the most transformative shifts will be in hyper-personalization, which will allow financial institutions to offer highly customized products and services tailored to individual customer behaviors, preferences, and needs. As AI capabilities advance, banks will increasingly integrate AI-powered chatbots and virtual assistants to enhance customer interactions, making them more intuitive and responsive. Ultimately, the banking and financial services sector will evolve into a highly intelligent, data-driven ecosystem where AI-driven insights enable smarter decision-making, leading to a seamless and more personalized banking experience.
Of course, let’s not overlook the fact that less than two years after Gen AI went mainstream, an equally—if not more—transformative breakthrough has emerged: Agentic AI. This innovation can potentially drive a paradigm shift in banking by replacing static chatbots and siloed automation with autonomous, decision-making AI agents that can adapt, learn, and execute tasks with near-human intelligence.
Many financial institutions are still in the early stages of AI adoption. Is it too late for late adopters to catch up, or do you see viable strategies for them to close the gap?
It’s not too late for financial institutions to embrace AI, but a strategic approach is essential. Late adopters should integrate AI into their strategic plans, focusing on high-impact use cases that offer immediate value.
A recommended first step is identifying “quick-win” opportunities—such as AI-driven customer service chatbots, document processing automation, and fraud detection—where AI adoption can quickly enhance efficiency and customer satisfaction.
Additionally, rather than developing AI models in-house, which can be resource-intensive and time-consuming, financial institutions can accelerate AI deployment through partnerships with specialized AI vendors. These partnerships allow banks to leverage pre-built AI models and cloud-based AI solutions, reducing implementation time and costs.
Another key strategy is leveraging low-code and no-code AI platforms. These platforms enable financial institutions to deploy AI-driven solutions rapidly without extensive technical expertise. For example, AI-based Intelligent Data Processing (IDP) modules can extract, categorize, and analyze data from unstructured sources such as PDFs and e-mails, improving operational efficiency.
By adopting these focused strategies, late adopters can efficiently close the AI gap while ensuring compliance and scalability.
What are the biggest challenges banks face when implementing AI solutions, and how can they overcome them?
When implementing AI solutions, banks face three primary challenges: Data quality, legacy system integration, and regulatory compliance.
- Data Quality & Availability: AI models require high-quality, structured, and diverse datasets for training. However, many banks struggle with fragmented data sources, lack of governance, and inconsistent data formats. To overcome this, financial institutions should invest in robust data governance frameworks, centralize data repositories, and utilize AI-driven data cleaning and enrichment tools.
- Legacy System Integration: Many banks still operate on legacy IT infrastructures not optimized for AI implementation. Deploying Application Programming Interface (API)-based integration architectures can bridge the gap between AI-driven solutions and legacy systems, allowing seamless data flow and process automation without a complete system overhaul.
- Regulatory and Compliance Concerns: The evolving regulatory landscape requires AI models to be transparent, explainable, and auditable. To address this, banks should implement AI governance frameworks, work with regulatory bodies, and adopt trustworthy AI solutions that prioritize transparency, bias mitigation, and compliance alignment.
By proactively addressing these challenges, banks can create a solid foundation for AI adoption and ensure long-term success.
Why is it critical for banks to experiment with AI use cases before full-scale deployment?
Experimentation with AI use cases is essential for banks to ensure operational feasibility, regulatory compliance, and successful user adoption before committing to full-scale deployment.
- Operational Readiness: AI-driven solutions can significantly alter workflows, necessitating process re-imagination rather than just re-engineering. Experimentation allows banks to assess the impact on existing processes and make necessary adjustments before widespread implementation.
- User Acceptance & Training: AI-driven systems require alignment with end-user expectations. Small-scale pilot projects help banks gauge user acceptance and refine AI models based on real-world feedback, ensuring smoother adoption and trust in AI-driven decision-making.
- ROI Validation: Banks must measure the financial and operational impact of AI before making large-scale investments. Experimenting with AI use cases—such as customer acquisition enhancements, cost savings in operations, and fraud prevention improvements—provides tangible insights into ROI, enabling data-driven investment decisions.
By taking an iterative, test-and-learn approach, banks can mitigate risks, refine their AI strategies, and ensure that AI adoption aligns with business objectives.
How should financial institutions approach AI readiness and deployment to maximize ROI?
For AI deployment to yield maximum ROI, banks must take a strategic, cross-functional approach that aligns AI initiatives with business objectives.
- Strategic Alignment: AI should be embedded in the financial institution’s broader strategic goals, such as cost reduction, process efficiency, customer acquisition, and improved risk management.
- Cross-functional Collaboration: AI implementation should not be siloed within IT teams. Instead, cross-functional collaboration involving risk, compliance, operations, customer service, and business units is crucial to ensure AI solutions address the needs of the entire organization.
- Workforce Upskilling & Change Management: Banks should invest in training programs to equip employees with AI-related skills. A smooth transition to AI-driven operations requires structured learning programs, hands-on training, and leveraging external AI talent through partnerships.
- Scalable AI Infrastructure: Financial institutions should deploy scalable AI platforms that allow iterative improvements and seamless integration with existing banking systems.
By prioritizing these elements, banks can ensure AI adoption drives meaningful business outcomes, enhances customer experiences, and delivers measurable financial returns.
Fraud in digital payments is a growing concern. How can generative AI help financial institutions combat rising fraud and improve security?
The increasing adoption of digital payments has led to more sophisticated fraud tactics. Gen AI offers enhanced fraud detection capabilities by analyzing vast datasets in real-time and identifying anomalous transaction patterns that traditional rule-based systems might miss.
Gen AI can:
- Detect fraud with greater accuracy by continuously learning from transaction data, reducing false positives while improving fraud prevention rates.
- Identify behavioral anomalies at an individual customer level, flagging suspicious activities based on historical trends.
- Enhance threat intelligence by scanning industry reports, security bulletins, and global incident databases to predict emerging fraud trends and adapt accordingly.
By leveraging these AI-driven capabilities, financial institutions can significantly strengthen their fraud prevention measures, optimize human-led investigations, and ensure more secure digital transactions.
WNS has co-created solutions across diverse clients to address the fraud detection and investigation aspects leveraging analytics and AI. These solutions leverage a combination of
Predictive modeling for fraud detection and classification.
- IDP for document digitization
- Deep Learning-powered solutions for detecting tampered documents
Compliance requirements are becoming more complex across global markets. How can AI assist banks in meeting regulatory standards more efficiently?
Regulatory compliance has been a dynamic and evolving challenge over the last few decades due to the constant shifts in global regulations. This has made it imperative for banks to invest in people, processes, and technology to ensure adherence to increasingly complex compliance mandates. AI has emerged as a powerful tool in streamlining regulatory compliance, particularly in tracking regulatory changes and assessing their impact on banking operations. By leveraging AI, financial institutions can receive real-time updates on regulatory changes, allowing them to proactively plan and implement compliance measures.
Additionally, AI-driven solutions can systematically organize and manage regulatory publications, reducing the manual effort required to track and interpret these changes. Gen (Gen AI) further enhances compliance capabilities by analyzing new regulations compared to previous versions, enabling institutions to swiftly assess their impact on internal policies and procedures. This capability strengthens risk and control assessments, making compliance processes more effective and efficient. Furthermore, Gen AI can be instrumental in creating structured training content, ensuring that staff remain well-informed on regulatory changes. AI-driven continuous monitoring and auditing capabilities significantly reduce compliance risks, providing institutions with a proactive approach to regulatory management and minimizing surprises in overall risk and compliance frameworks.
In the area of compliance, WNS has been at the forefront of building solutions leveraging AI & Gen AI over the last few years including:
- Gen AI Solution for Adverse Media screening
- AI & Gen AI solution for AML Investigations
- AI-powered solution for enhanced KYC
What are some of the most promising AI-driven use cases in banking today, beyond fraud detection and compliance?
AI is continuously evolving, with data scientists and AI practitioners developing innovative solutions to address a wide range of business imperatives in the banking sector. Beyond fraud detection and compliance, AI is driving significant transformation in customer experience, operational efficiency, and decision-making processes.
One of the most prominent use cases is AI-powered chatbots and Gen AI-driven real-time agent assistance, which are revolutionizing customer service by providing personalized, instantaneous, and efficient interactions. This shift enhances customer satisfaction and streamlines issue resolution.
On the operational front, AI is addressing longstanding challenges in reconciliation processes, which typically span multiple departments within financial institutions. AI-driven algorithms can automate the matching of unmatched transactions by analyzing historical patterns and trends, significantly reducing the need for manual intervention.
Another critical application is payment processing, particularly handling incoming deposits where incomplete or ambiguous payment details require human intervention. AI-driven propensity models can generate likelihood scores, helping institutions quickly and accurately identify the correct counterparties and thereby optimizing manual processing efforts.
These are just a few examples of how WNS has actively supported leading financial institutions in leveraging AI to drive efficiency, improve customer experience, and enhance operational resilience.
AI talent is in high demand across industries. What can banks do to attract and retain top AI professionals?
The increasing adoption of AI across industries has led to a surge in demand for AI talent, particularly data scientists and machine learning experts. Attracting and retaining top AI professionals requires a strategic approach centered on providing meaningful opportunities and fostering an innovative environment.
One of the primary motivations for AI professionals is access to high-quality data that allows them to experiment, innovate, and develop impactful solutions. Organizations that offer structured, well-organized data ecosystems create an environment where AI experts can thrive. Conversely, the lack of opportunities to create meaningful business impact is one of the key reasons AI professionals leave organizations.
To attract top talent, banks must establish strong partnerships with leading engineering colleges and universities, offering AI-focused internships, research collaborations, and direct recruitment pathways. This approach enables financial institutions to engage AI professionals early in their careers and provide a clear growth trajectory within the organization.
Additionally, fostering an AI-driven culture within banks—where AI professionals can work alongside domain experts to solve real-world challenges—enhances job satisfaction and retention. WNS has successfully implemented a similar model in collaboration with premier universities in India, ensuring a steady pipeline of skilled AI talent ready to contribute to the financial services sector.
How do you see the role of banking professionals evolving with AI integration? Should employees be concerned about job displacement?
The discourse around AI replacing human jobs has been prevalent for years. While AI does automate routine tasks, its true impact lies in augmenting human capabilities and enhancing the quality of work performed by banking professionals.
As repetitive and rule-based tasks are increasingly managed by AI and Robotic Process Automation (RPA), banking professionals are shifting toward roles that require higher levels of judgment, critical thinking, and decision-making. The shift has unfolded over the past decade, with digital payments steadily replacing traditional check-based transactions. While this transition has reduced branch and ATM footfall, it has simultaneously led to branch staff taking on more complex roles, such as payment exception management, investment advisory, and fraud investigation.
At the back-office level, AI has simplified operational efforts by digitizing unstructured data from PDFs and scanned documents, previously processed manually. Human involvement is now primarily focused on exception handling and complex investigative tasks, thereby improving efficiency and accuracy.
Rather than causing job displacement, AI-driven transformation has led to talent upskilling, ensuring that professionals develop new competencies that align with industry needs. This shift ultimately benefits the financial services sector by fostering a workforce equipped with advanced analytical and problem-solving skills.
Are there any best practices you’ve observed in fostering an AI-first culture within banking organizations?
WNS has been at the forefront of fostering an AI-first culture across its diverse banking engagements, driving AI-led transformation through a structured, collaborative approach. Several best practices contribute to successfully embedding AI within banking organizations:
- Co-creation Model: Banks must integrate domain experts and data scientists within operational teams to ensure AI-driven solutions are aligned with real-world banking challenges. At WNS, this approach has enabled the identification and execution of hyper-automation opportunities, ensuring continuous improvement.
- Embedded Transformation Teams: AI transformation efforts should not be siloed within IT or innovation departments. Instead, embedding transformation teams within core operations ensures sustained focus on identifying AI-led efficiencies and scaling solutions across the organization.
- Cultural Awareness and Adoption: Organizations must invest in AI literacy programs to educate employees about AI’s benefits, use cases, and potential to augment their roles. A well-informed workforce will more likely adopt AI solutions and contribute to their successful implementation.
- Incentivizing Innovation: Encouraging employees to contribute AI-driven ideas and rewarding successful implementations fosters a culture of continuous improvement and engagement. Competitions, hackathons, and recognition programs can help drive AI adoption at all organizational levels.
By adopting these best practices, banks can ensure seamless AI integration, maximize operational efficiency, and build a workforce that is equipped for the future of financial services.
Thank you for the great interview, readers who wish to learn more should visit WNS.