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I’m working at the intersection of actuarial science, advanced analytics, and health policy to tackle one of the most stubborn problems in U.S. healthcare: the broken flow of money between patients, providers, and payers. My project, Transforming U.S. healthcare finance through a technology‑driven, actuarial, and predictive analytics framework for Revenue Cycle Management (RCM), is focused on turning today’s reactive, fragmented billing systems into an integrated, predictive engine that keeps providers solvent and care affordable, especially for vulnerable and rural communities.

What excites me is the chance to move beyond describing the problem to engineering a solution. I see every denied claim, delayed reimbursement, and preventable write‑off as a data point that can be modeled, predicted, and ultimately prevented. With my background in Actuarial Science and Business Analytics and Information Management, I’m building a platform that ingests data from EHRs, claims, and billing systems, then uses actuarial risk models and machine learning to forecast cash flow, flag improper payments in advance, and optimize how and when providers get paid. The same tools that insurers use to price risk can be repurposed to protect access to care, stabilize hospital finances, and relieve pressure on premiums and out‑of‑pocket costs.

This work is a game changer because financial inefficiency is not just an accounting issue, it directly shapes whether clinics close, whether patients can find primary care, and whether reforms like value‑based care actually succeed. By turning RCM into a proactive, predictive, and transparent process, I aim to give providers timely, reliable revenue; give payers cleaner, more accurate claims; and give policymakers a practical lever to support affordability and access goals like those being pursued in primary care reform and cost‑containment efforts.