Using AI for Financial Planning in Health Insurance

Editor’s note: This is part two of the interview on digital transformation in health insurance budgeting. In part one, ScienceSoft’s Olga Vinichuk outlined top automation tools that could replace spreadsheets for budgeting. Here, she goes into detail about using artificial intelligence (AI) for accurate and efficient financial planning in health insurance. The interview is led by Stacy Dubovik, a financial technology researcher.

How Health Payers Benefit From AI for Financial Planning — And Do They, Actually?

SD (Stacy Dubovik): Statistical analysis and rule-based automation have worked well for decades in insurance budgeting. Can AI actually improve much?

OV (Olga Vinichuk): In short, absolutely yes. AI offers dramatic gains in efficiency for tasks like data processing, tracking financial trends, calculating reserve requirements, and adjusting operational budgets in response to changes. Gartner’s analysts have recently estimated that combining AI with traditional automated financial planning can potentially remove more than 90% of manual budgeting workflows.

In financial analytics, machine learning (ML) algorithms can pinpoint subtle correlations between diverse variables like seasonal health policy demand, disease-induced claims, shifts in healthcare costs, reinsurance rates, and even promotional campaigns by drug manufacturers. ML models can forecast financial KPIs at a scale and speed unattainable with traditional statistical systems. Accuracy benchmarks speak for themselves: in ScienceSoft’s projects, we managed to deliver predictive ML models offering 90–95%+ precision.

SD: Where does AI shine the most in this field?

OV: AI truly shines in budget variance analysis. Intelligent engines can diagnose multiple budget utilization factors simultaneously and give clear insights into the root cause of gaps. Real-time scenario modeling is another critical strength. With an ML-powered tool, you can simulate the financial impacts of shifts in enrollment, spikes in health claims, and other tactical variables on the fly and quickly enforce the necessary budget corrections. One more area where AI works effectively is budgeting bias detection. For example, your finance teams might under-reserve claim funds for a certain group (e.g., young insureds without preexisting conditions) because of the incorrectly perceived group’s risk. AI could cross-reference manual projections with underwriting data and historical claim payments to conclude whether the segment-specific reserves are reasonable.

AI-powered virtual assistants can expedite your finance team in quicker financial planning, variance analysis, and forecast refining. The best thing about AI copilots is intuitive employee experiences. Your analyst can just ask a regular question like “What is the main driver of last month’s budget fluctuation?” to get suggestions on inefficient allocations and ways to bridge variance gaps. Microsoft AI Copilot service now enables smart assistants to be brought to any Microsoft product, meaning you can introduce this feature to your team’s much-loved Excel spreadsheets.

Is AI Smart Enough to Budget Health Insurance Autonomously?

SD: I heard that AI can even prescribe the optimal loss and reinsurance reserves. Is it generally safe to rely on AI for health insurance budget construction?

OV: As always, AI is not — and should not be — an all-knowing, autonomous decision-maker in health insurance budgeting. While AI can analyze big datasets, spot patterns, and make data-driven suggestions, it needs to work alongside your finance teams to ensure the final budget factors in the nuances of your business and external events.

The primary reason to treat AI as an assistant, not a decision-maker, is the inherent limitations of its predictive scope. Intelligent models are trained on historical and current data, meaning that however “skilled” they are at extrapolating trends, they cannot anticipate unprecedented changes. A prime example is the pandemic: intelligent models that were excellent at projecting claims patterns pre-2020 were caught off-guard when healthcare consumption and spending dramatically changed because of COVID-19. Human judgment remained critical for accurate claim forecasting.

As for the loss reserves, AI excels at predicting everything from regional medical cost trends to the potential impact of emerging diseases or treatments. Say you notice an unusual increase in claim costs associated with new high-cost therapies for autoimmune disorders. ML models might flag reserve leakage and recommend optimal funding based on similar historical trends and healthcare provider behaviors. However, what AI cannot do is account for certain external, qualitative factors that may affect reserve volumes, such as shifts in medical regulations or anticipated renegotiations with healthcare partners.

Similarly, for reinsurance reserves, AI can crunch probabilities on high-cost claim risks in a way traditional methods struggle to match. For example, smart algorithms might use claim histories, demographic data, and catastrophic claim distributions to calculate the level of reinsurance coverage for specific populations and suggest the optimal proportions of risk to retain vs. to cede to reinsurers. However, AI fails to consider strategic insights like future changes in reinsurance premiums unless your team explicitly incorporates this data.

Ways to Ensure AI Transparency in Health Insurance Finance

SD: AI is known for its opaque logic, but health payers need complete budgeting transparency. How do you usually handle it?

OV: My colleagues from ScienceSoft’s data science team say there are ways to make AI projections transparent and interpretable without compromising their value. They apply explainable AI (xAI) techniques that shed light on how smart budget predictions are made. This lets our health insurance clients understand the drivers behind every analytical output and ensure budget compliance with ACA and IFRS requirements.

For example, we commonly employ the LIME technique for localized explainability. Let’s say AI predicts unusually high claim losses for your gold-tier plans compared to other tiers. LIME algorithms could generate a breakdown of why this specific trend outstands. For instance, they could highlight patterns in chronic disease prevalence or increased use of prescription medicines among premium customers.

The feature importance scoring technique helps determine the variables that have influenced AI outputs the most. For instance, if AI predicts a spike in claim costs, you might see that the uptick is driven primarily by shifts in healthcare provider costs. With this early insight, you could proactively focus on negotiating partner discounts and seeking reinsurance options.

To maximize transparency, an AI budgeting tool should be designed to log each step of the predictive process, from financial data preprocessing to reasoning. This will let your AI model management team view how the data was treated, what assumptions were applied, and how those influenced the final results. For example, if you’re reviewing a loss projection, you can examine exactly how AI weighted historical claims versus external variables like changes in regional demographics.

We also apply auto-validation rules to double-check AI-generated suggestions against the insurer’s budgeting policies and regulatory requirements. For example, if AI recommends loss reserves that contradict established guidelines or benchmarks, the solution flags the output for human review and future model tuning.

If you need advice on bringing AI into your health insurance financial planning workflows, feel free to contact me or other consultants at ScienceSoft.