The Evolution of Customer Centricity in IT Professional Services – an Outlook of the Fintech Industry in the Era of AI

Executive Summary

With the exponential advancement in AI over the past few years, customers demand more of financial organizations and banks. In turn, most financial institutions understand today that what success was achieved over the past decades will not continue unless there is a seamless integration with technology. Cryptocurrencies, virtual banks, and digital-only financial platforms are challenging traditional institutions to offer more to their customers. While conventional financial institutions have the stability, reliability and reputation, modern high-tech services make a compelling case of easy swift access to users, making the speed and easiness tempting not to use heavily regulated banking systems.

The only way forward for these institutions to retain and grow their customer base is a swift yet well-designed adoption of many evolving Fintech and AI initiatives. This includes hyper-personalized banking experience, customer insights, cost-effectiveness, privacy, information security, personal portfolio management, customized dashboards while maintaining compliance and governance from the other side.

Customer centricity is no longer an option but a must. Today’s financial institutions have to embrace their customers at the center of all decisions, providing easy, fast, reliable, data-driven, secure services. In the Fintech industry, customer centricity starts with the Professional Services — including consulting, implementation and deployment of services — that link financial services and products with cutting-edge technology in security, privacy, simplicity and ease of use, along with the insights and predictive analysis of AI.

This article explores the evolution and modern trends of Professional Services and consulting as the driver of customer centricity in an era of public and hybrid cloud services that are easy to access and provide unprecedented flexibility outside conventional banking institutions.

The Evolving Trends of Professional Services and Customer Centricity

1. Introduction:The Professional Services organization was classically based on three main pillars: Consulting (advisory), Implementation (building, configuring, integrating, and testing), and Deployment (moving to live production environment). In addition, Professional Services may also include managed services, support services, training / enablement, optimization, and continuous improvement.With the evolution of AI, most Professional Services pillars are also evolving — not only in the sense of what is offered to the customer in evolving AI technology, but also how it is offered to banking customers through Professional Services. As customer expectations evolve, and competition becomes fiercer and technology-dependent, the Professional Services function also has to evolve in how it is able to consult for both the banking institutions and their customers across different banking segments.
2. Professional Services Evolving Trends for Enhanced Customer Centricity
   1. The advisory role played by Professional Services in the selection and customization of LLMs (Large Language Models) and GenAI (Generative AI). With the needed customization to enable chatbots and virtual assistants, the PS work ensures that financial institutions maintain compliance, risk management, and data privacy.
   2. The design and mapping of back-office processes, including the selection of RPA (Robotic Process Automation) and document processing, to ensure that routine and repeatable tasks are done seamlessly and accurately, increasing efficiency and decreasing internal friction within banking systems and teams.
   3. The design and implementation of automation of fraud detection systems and risk scoring, including the auditability of systems and data, in alignment with compliance and privacy rules.
   4. The design of AI analytical models assisting with segmentation and profiling to transform the customer journey into an integrated and hyper-personalized experience. This includes the design of testing, validation, and privacy considerations.
   5. The design of data strategy and AI platforms, including cloud migrations to aid in developing cleaner, centralized, federated data infrastructure with the purpose of breaking silos and improving data quality.
   6. The design and implementation of AI governance models to ensure ethics, bias-free operation, and fairness. This is exceptionally important for legal and regulatory governance and ensuring risk compliance.
   7. The design of AI evaluation systems to evaluate, score, and help select partners and vendors, ensuring interoperability and oversight of third parties.
   8. The design of tools to help increase and augment banking staff, ensuring alignment of workflows, training, and measuring the bank ROI.
   9. The design of AI systems that combine different types of media (voice, text, images, and structured data), building pipelines of “multimodal” data — providing an integrated customer experience and enabling a virtual assistant to analyze a customer’s voice, account text data, and submitted photo ID simultaneously for seamless verification.

Leadership Implications

Considering the above and the numerous applications of AI in Fintech, the following becomes a necessary focus for technology and professional-services leaders:

1. Strategic alignment of AI initiatives across different banking verticals and functions.
2. Built-in governance and risk management designed from the exploratory phases to ensure embedding of these capabilities from the start.
3. Ensuring data-readiness by designing clean, well-governed, and secure data.
4. Design of clear metrics to enable visibility of ROI to financial institutions, including cost saving, risk reduction, and customer satisfaction.
5. Security and compliance of third parties and vendors.