Vijay Pandey leads Wipro’s Financial Service Ops, excelling in service delivery, project execution, and leadership.
By infusing domain-specific knowledge into a standard AI framework, we can create responses that are finely tuned to specific industries or processes. This refined AI approach is referred to as “vertical AI,” which provides a concentrated understanding of the complexities inherent in financial operations, resulting in more informed decision-making.
Vertical AI is emerging as a groundbreaking force in the banking and financial services sector, specifically designed to cater to distinct areas such as lending, payment processing and the fight against financial crime. While many are accustomed to general-purpose AI tools like Copilot, ChatGPT and Gemini, these solutions often lack the specialized context necessary for practical applications in banking.
The predictability and reliability of vertical AI make it exceptionally suited for real-world applications. For example, in the lending space, vertical AI can swiftly evaluate creditworthiness by recognizing patterns that traditional methods might overlook. This capability accelerates loan approvals while minimizing risks, allowing banks to present more competitive and tailored loan offerings to their customers.
Industry Adoption Of Vertical AI
There are several notable advantages associated with vertical AI in financial operations.
A report by the Boston Consulting Group indicates that incorporating vertical AI for risk and compliance checks has resulted in a greater than 50% cost benefit, ensuring that financial institutions remain compliant with regulatory standards while maintaining high accuracy.
Vertical AI empowers institutions to harness crypto’s potential without compromising on security or compliance standards. By integrating insights from top research and consulting firms, it’s evident that vertical AI not only enhances operational efficiency but also sets new benchmarks in the financial industry’s service standards.
A large bank and payments company improved policy and procedure search time by up to 70% for their 10,000 contact center agents. This means a 70% reduction in call handling time and unmatched customer experience.
McKinsey estimates that generative AI could add $200 billion to $340 billion annually in value across the banking sector, primarily through improved efficiency and innovation.
KPMG estimates that implementing AI in banks and financial service institutions can lead to significant cost savings by reducing fraud rates. Specifically, AI can help reduce false positives by up to 60% and make know your customer processes 90% faster, allowing institutions to handle the same volume of work with fewer resources.
Integrating an AI tool into the lending fulfillment ecosystem brings about transformation beyond mere efficiency gains; it redefines customer interactions by delivering hyper-personalized services at an unprecedented scale. As consumers increasingly seek installment credit solutions aligned with their spending habits or desire tailored advice derived from real-time data analysis, vertical AI is not just refining operations; it is establishing new service standards across the industry.
The Role Of Vertical AI In Banking, Lending And Payments
Fintech companies focusing on vertical AI are revolutionizing traditional banking, lending and payment practices by implementing advanced workflows that address long-standing industry challenges. For instance, when utilized for underwriting and verification processes, vertical AI excels in document and data processing.
It efficiently analyzes large volumes of PDFs and extensive datasets to accurately match products with customer profiles, ensuring seamless compatibility between clients’ needs and financial offerings. This precise matching capability far surpasses what was achievable with earlier technologies like robotic process automation or machine learning. The result is a significant reduction in human error and bias during decision-making processes.
Vertical AI’s contextual focus yields substantial improvements in managing critical manual tasks such as claims processing and dispute resolution through enhanced fraud detection capabilities. By integrating banking platforms with intelligent checklists and compliance measures, these solutions will meticulously examine transactions for suspicious activities, leading to quicker dispute resolutions, decreased instances of fraud and reliable compliance with regulatory standards—all without sacrificing efficiency.
Challenges Facing Adoption
In an industry where accuracy is paramount, vertical AI’s ability to perform precisely focused calculations instantaneously fosters faster, more secure transactions that offer control and predictability—essential elements in today’s data-driven landscape. However, adoption of the technology is not without challenges such as:
Data Quality And Availability: Inaccurate, incomplete or biased data can lead to flawed AI models.
Regulatory Compliance And Scrutiny: Navigating complex regulatory landscapes for approvals and clearance can be difficult.
AI Explainability And Bias: Developing and implementing a vertical AI solution that is predictable and follows appropriate process guardrails takes time and careful planning.
Talent Scarcity: Getting the right talent onboard such as data scientists, AI engineers and professionals with required domain expertise is a challenge.
Working With Legacy Systems: Solving fragmented systems workflows and handoffs, outdated technology and a complicated tech stack is to be remembered.
Recommendations For Solving Adoption-Related Problems
Vertical AI adoption starts by answering the three W’s: what, where and when. Answering these W’s will allow you to create an execution plan.
Then proceed with moderation. A gradual phased approach helps reduce anxiety and stress in the team and drives success. Your execution plans should be drafted in sequenced steps so that it’s manageable and tasks are not mingled.
To ease the concern stakeholders and regulators may have about the vertical AI’s use, include policies broken down into tree diagrams or mind maps for visible logical workflow. Then select a well-established and robust partner to solve talent and technology needs. Ensure the partner ecosystem is strategically aligned with yours.
Like any change, the process of implementing a vertical AI requires communication. Driving a positive and innovative culture, offering training and clear articulations on the benefits of AI adoption will make it easier to deploy.
Lastly, don’t wait to fix the LLMs vectors, rather use the shelf LLMs to comprehend inputs, a database of rules and policies to apply guardrails, and a basic API to direct actions to systems, i.e., the simplest and most basic concept of retrieval-augmented generation.
Are you ready to transform your operations with vertical AI?
The information provided here is not investment, tax or financial advice. You should consult with a licensed professional for advice concerning your specific situation.
Forbes Finance Council is an invitation-only organization for executives in successful accounting, financial planning and wealth management firms. Do I qualify?
