Significantly Reducing Turnaround Time, Improving Claim Approval Rates And Customer Satisfaction

The adoption of an AI-driven health care denial predictor has resulted in a significant improvement in customer satisfaction, approval rates, and turnaround times for claim approvals. This innovative solution, led by Karthika Gopalakrishnan, marks a transformative shift in the way banks handle healthcare claims, showcasing the powerful potential of AI and machine learning (ML) in streamlining complex processes.

Karthika Gopalakrishnan, currently serving as Director Consulting Expert in Data Science, has played a critical role in this advancement. With a solid background in AI/ML applications within the banking sector, her contributions have been instrumental in redefining traditional banking operations through cutting-edge technology.

The Health Care Denial Predictor emerged from a pressing need to expedite the often lengthy and error-prone claim processing procedures. Typically, the claims process involved a prolonged wait, frequently ending in rejection due to incomplete or incorrect information such as missing diagnosis codes. Understanding this challenge, Gopalakrishnan and her team set out to create an AI/ML solution that not only accelerates the approval process but also intelligently identifies and communicates potential reasons for claim denial.

By leveraging client data, the team developed the Denial Predictor, which accurately predicts the likelihood of a claim being rejected and specifies the reasons behind it. This predictive capability enables users to address issues proactively, thus reducing the processing cycle from an average of 14 days to just a few days. The result is a tool that enhances efficiency, reduces operational costs, and boosts customer satisfaction by minimizing delays and uncertainties in claim approvals.

In her role within the Innovations team for a major banking client, Gopalakrishnan has been a cornerstone in architecting AI/ML solutions tailored to solve intricate business problems. Her expertise extends beyond technical implementation; she has also been a mentor to junior data scientists, guiding them through real-time projects and fostering a culture of innovation and learning.

One of Gopalakrishnan’s notable projects includes the development of an Intelligent Document Processing Platform, which alongside the Denial Predictor, showcases her ability to harness AI/ML to automate and optimize banking operations. These projects reflect her strategic approach to integrating advanced technologies into the banking sector, driving both immediate and long-term benefits.

The quantifiable impact of the Denial Predictor is significant. By providing clear reasons for claim rejections, the tool has drastically cut down the claim processing time, enhancing operational efficiency. This improvement not only reduces costs associated with prolonged processing but also increases the approval rates by ensuring that claims are accurate and complete upon submission.

However, the journey to this success was not without its challenges. One of the primary hurdles was dealing with the quality and readiness of the data. Gopalakrishnan and her team faced difficulties in ensuring the availability and integrity of decades-old data, which was not initially AI/ML ready. Through persistent efforts, including extensive preprocessing and model tuning, they were able to develop a robust solution that adapted to different data types and formats, ensuring reliable predictions across various scenarios.

Karthika Gopalakrishnan has also contributed to the field through her published works and media coverage, sharing insights and advancements in AI/ML applications in banking. Her thought leadership extends to advocating for a deeper understanding of data and clear communication with business stakeholders to align expectations and ensure successful AI/ML implementation.

Reflecting on her experiences, Gopalakrishnan emphasizes the importance of educating business operations and management about AI/ML models. She notes, “Models have their own biases and are not always 100% accurate. Clear communication with the business is essential to build trust and work together for an effective solution.” Her insights underline the necessity of aligning technological solutions with business needs and maintaining ongoing collaboration to refine and enhance these models.

The development of the Health Care Denial Predictor stands as a testament to the transformative power of AI/ML in the banking industry. Led by Karthika Gopalakrishnan, this innovative solution significantly reduces turnaround times, increases claim approval rates, and improves customer satisfaction—all while showcasing the significant impact of technology in streamlining banking operations and setting new industry standards for efficacy and efficiency.