Banking & Financial Services • June 15, 2023

AI/ML in Financial Services and Insurance: Key Considerations for a Successful Business Transformation

Artificial Intelligence (AI) and Machine Learning (ML) affect most businesses, especially organizations where a high degree of regulation, standardization, and data processing exist. Financial services and insurance (FSI) organizations need to adopt and deploy these technologies at scale to stay relevant in a rapidly evolving market. AI adoption enables FSI firms to meet strategic goals, such as improving customer experience, operational efficiency, and speed-to-market for the offerings while driving personalization. 

In recent years, Quantiphi has achieved unprecedented success in building systems for the FSI and mortgage industry. The present AI-enabled digital age is driving organizations to leverage these technologies to integrate and make sense of complex data, achieve higher automation, and boost revenues. Many FSI organizations, however, struggle to scale AI technologies across the organization. This blog post discusses how to accelerate your digital transformation and become an AI-first organization.

Why must you become AI-first?

Rising customer expectations, evolving regulations, technological advancements, and increasing competition are rapidly altering the FSI landscape. Organizations that fail to adopt and scale AI are at a huge risk to be overtaken by the competition. Recent times have also witnessed most FSI businesses undergoing significant resource attrition, and customers turning away to competitors for a better experience.

AI/ML improves customer satisfaction by increasing personalization, reduces costs, and uncovers new opportunities. AI/ML reduces repetitive tasks for resources, maintains standards across the organization, and improves overall quality. It can also improve SLA by boosting the efficiency of processes and freeing up resources to focus on the steps that require human interaction. 

How to get started

Adopting AI/ML involves a learning curve as it is not merely a ‘technology’ project. It is a business transformation project that uses technology as a tool to drive change. Quantiphi has encountered multiple instances where operational units assumed that there are standardized processes or policies that facilitate the rapid transition to AI/ML automation. However, once the actual implementation begins, it becomes evident that the transformation is at the organization level with multiple layers and stakeholders. Hence, the first rule to get started on your digital transformation journey is to break down the project into smaller steps and understand how these pieces fit together.

The process typically involves the following phases:


Begin with analyzing the existing state of the business value chain and consider the interests of the involved teams. For instance, if the teams depend on scheduling software to assign operational resources and ensure that the processing service meets the required standards, they need AI/ML tools. The internal stakeholders have insights into their functions and they are instrumental in laying out the transformation objectives and roadmap. 

A strategic ‘discovery session’ works best in such scenarios. Most of the companies that Quantiphi assists find it helpful to lay out a high-level plan or priorities. This can be a few days or a week-long session to evaluate opportunities in the organization where AI/ML tools can add value. The outcomes of this session may include a heat map of opportunities that shows the areas of greatest need, a high-level roadmap or plan showing the direction the organization wants to take, and change management recommendations that need to be considered while building the plan.

Proof of Concept

Once the starting point is clearly defined, it is advisable to have a "proof of concept" (POC) before implementing it on the roadmap. For example, in FSI, one may pick a particular policy type to underwrite or service, a particular claim type or coverage type, or a particular state. This POC helps stakeholders to better understand the requirements for a successful full-fledged project. 

It is important to consider it as a business project and not only a ‘technology project’. Business expertise and ongoing input are critical to success. While the technology may be managed by IT, the rules and operational knowledge are brought in by the business users and leaders. The compliance, regulatory, and legal teams should also add inputs to refine business rules or process steps and turn the POC into a successful project. 

Transformation Management

POC provides a better understanding of the requisites and pillars of the business transformation. This mode of working is radical and is usually met with a certain degree of concern and skepticism. People fear that automation will take their jobs away. The best approach to overcome these fears and propel the business forward is to have a plan in place for communicating the changes and bringing the team members on board. A step in the right direction is to involve early adopters from the operations area who are affected by the project. These frontline stakeholders can provide real insights into how things work, what can be achieved, and how to share the potential change with their peers.

The human factor remains the most important aspect of any successful AI/ML endeavor. AI/ML is a powerful tool that can perform tasks like processing and managing huge volumes of data, and extract valuable insights from it. However, it cannot make decisions or replace the human touch. We advise our customers to automate what you can and let people use their creativity and judgment to build solutions and serve customers better.

Test and Learn

Even with considerable expertise in technology, an AI/ML project can pose unique challenges and unprecedented situations. Tackling such challenges hands-on provides invaluable insight and experience. It is important for the organizations and stakeholders to allow such learning experiences along the way. At Quantiphi, we are open to "test and learn." Initial failures are part of the process and are treated as learnings. An organization may need several cycles of testing and learning before arriving at a final solution. This is where a POC proves of immense value.


It is vital that organizations that intend to infuse AI/ML into their processes find a seasoned partner who has navigated the road you intend to take. AI/ML projects are unique and transformative. While many businesses have internal teams who know the basics, rare few have the level of expertise required to conduct a full-scale transformation. The right partner can help the organization identify where to start (discovery), how to plan and execute a POC, and handhold the team through the ‘test and learn’ phases. A partner like Quantiphi can also offer advisory services to help with the AI/ML transformation journey, bringing expertise that helps you identify the changes that may occur on the way. 

The Quantiphi Approach

Recently, Quantiphi helped a Fortune 500 American insurer to transform its claims processing. The customer processes millions of claims annually and desires to move to a more effective service model to enhance accuracy in making payments, improve consistency and increase automation. Previously, there was a significant reliance on claims team members to manually review documents, policy, and claim manuals to arrive at the final decision. 

Quantiphi successfully developed an end-to-end Claim Adjudication Platform to extract, classify, annotate, and index the documents submitted for proof of loss and other relevant information. Additionally, a predictive analytics solution was built to evaluate whether the claim can be paid or denied along with an AI assist for adjusters to finalize the eventual payout to communicate with the customer. 

This was one of the initial projects where Quantiphi successfully implemented MLOps in a human-centric, automated, and secured way of productionalization. It covered multiple facets such as a human-in-the-loop for model/dataset/workflow reviews, vulnerability management, data governance for access management, security, and automated, continuous active learning. The concrete business impact generated for the insurer included reduced turnaround time, significantly improved error rates, and increased operational efficiency, saving cost and delivering a superior experience to the customers and adjusters.
AI/ML has become a strategic imperative to build the financial services and insurance organization of the future. The positive impact on users, customers, and the business is driving AI adoption, making it vital to the business transformation roadmap. To accelerate your digital transformation journey or adopt AI at scale, reach out to our experts.

Written by

ShawnMarie Frazier

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