Chintan DhanjiActuary and former Bain case team leader
I help healthcare and financial services leaders grow revenue, improve profitability, and decide where to invest. I bring 20 years of commercial judgment, an actuary's understanding of risk and business economics, and the ability to build the models and lead the work myself.
You need to grow revenue, improve profitability, or justify a major investment. You need a clear answer on where to focus, what to spend, and how to make it happen.
You may have too many options, no obvious route, or a revenue stream under threat. I identify where growth can come from, model the alternatives, and turn the strongest path into priorities your team can execute.
I trace what drives cost, capacity, and service performance, then build the model to show which changes make a difference. That can mean redesigning claims operations, improving cash and collateral use, or changing how work gets done.
Build, acquire, partner, or buy a platform? I compare the commercial opportunity, investment required, downside, and implementation demands so you can choose what deserves funding.
I help determine which opportunities warrant investment, test a practical use case, and connect the technical evidence to workflow adoption, business value, and governance.
I understand how pricing, liabilities, claims, cash flows, and capital interact. I use stochastic and financial models to compare possible outcomes, test the assumptions, and show which path offers the strongest chance of meeting the objective.
I have built growth strategies, assessed acquisitions, designed insurance products, and run businesses. I use that experience to challenge assumptions, identify opportunities worth funding, and recognize when the right recommendation is to stop.
I build financial and machine learning models, work through the operational detail, and lead teams through the changes the strategy requires. I stay close to the evidence and the people who need to act on it.
You get a clear recommendation, the analysis behind it, and someone who can help turn it into results.
My work spans health-plan claims economics, workforce capacity, provider operations, federal healthcare growth, and enterprise AI adoption. I understand how operating performance and insurance economics shape the business.
My experience includes insurance pricing and liability valuation, treasury and collateral strategy, acquisition diligence, and an ML investment research system. I connect the financial mechanics to capital allocation and business performance.
Growth plans, better operating economics, and investment decisions backed by work I have led and models I have built.
Healthcare focused federal contractor
Leadership wanted to double revenue in five years. They needed to know where that growth could come from and which contracts and capabilities deserved limited business development resources.
I mapped the federal market against the organization's capabilities and customer relationships, then built a stochastic financial model to develop and compare alternative growth paths. It accounted for uncertain contract wins, rebid risk on existing revenue, capability gaps, and capture constraints.
The analysis identified the combination of contract pursuits, internal capability development, acquisitions, and partnerships most likely to achieve the target within those constraints. I translated that path into customer priorities, a capability roadmap, and a Balanced Scorecard.
Outcome: A roadmap projecting 2X revenue in five years, with a model leadership could update as opportunities and probabilities changed.
Read the federal growth caseIdentify the mix of pursuits and capability investments with the strongest modeled likelihood of reaching the growth goal within available resources.
Global alternative asset manager and retirement-services affiliate
The challenge went beyond choosing a platform: daily treasury decisions depended on data, processes, controls, and responsibilities spread across systems and teams.
I led the business operations workstream, evaluated the collateral platform build-versus-buy decision, developed the value case, and designed pilot and rollout plans. Follow-on work mapped 64 cash-flow line items and 10 collateral flows into the processes, data dependencies, and ownership needed for the new treasury platform.
Value quantified: Approximately $11.5M in potential annual value across five levers in the liquidity, cash, and collateral transformation.
The annual value figure is a quantified opportunity, rather than a claim of realized savings.
Health plan serving self-insured employers
Leadership needed a defensible view of staffing and cost as the business moved between claims platforms. Claim counts alone missed the work created by reprocessing, manual handling, backlog, and service tasks.
I developed a driver-based workforce model connecting covered lives and claims demand to automation, rework, measured handling time, and effective examiner capacity. The model separated the two platforms, ongoing workload from backlog clearance, and examiner work from provider repricing queues.
Decision support: A traceable baseline and scenario model to test how enrollment, automation, productivity, and platform migration change staffing requirements.
Covered lives and claim frequency determine demand. The manual share, rework, and time required determine workload. Effective capacity converts workload into staffing; staffing and unit costs provide the basis for cost to serve.
I reconciled claims records, production logs, and queue inventories so missing flags, incomplete logging, or provider repricing work would not silently distort the staffing answer.
Private fund manager
The fund could manually evaluate only three or four stocks a week. I built a CatBoost screening model across approximately 2,600 equities using 20 years of fundamental and macroeconomic data, then connected it to automated filing analysis, valuation, research memos, and watchlist monitoring.
Outcome: Systematic coverage of the stock universe, research memos on demand, and reported benchmark outperformance over a historical twelve-month period.
The system combined institutional filing analysis with ML screening, followed by research on financial fundamentals, management, competition, and risk. Research outputs were cross-checked against external valuation references and monitored against defined price triggers.
The design separated data gathering, model output, and the portfolio manager's decision so the process could support investment judgment at greater scale.
Fortune 500 healthcare company
I built a machine learning model to categorize contract data at 90%+ accuracy, securing leadership sponsorship for a wider AI strategy engagement. I then evaluated 50+ use cases against impact and feasibility and designed the pilot and scaling approach.
Outcome: Ten pilots approved within 90 days, each with projected ROI above 25%.
Read the healthcare AI caseGlobal open-source technology company
I modeled the company's exposure as AI changed search behavior, separated the impact on commercial searches from information gathering, and compared options to defend and diversify revenue. The strategy combined opportunities in existing products with new AI capabilities and monetization.
Outcome: Three monetization projects launched within six months, supported by a scenario-based roadmap projecting a path to revenue stabilization.
Read the revenue strategy caseFor a Fortune 500 healthcare subsidiary, I directed diligence and Day 1/100 integration planning for an approximately $300M acquisition. I built the financial model used in negotiations and assessed cross-product opportunities and operational synergies as new evidence emerged, connecting the investment decision to the work required after close.
We start with the result the business needs, work out what will drive it, and build the evidence and operating plan to pursue it.
Define what success means, what stands in the way, and the decision leadership needs to make.
Work through the market, financial, and operational evidence. Build the model, test assumptions, and quantify the options.
Choose the path, investments, and changes with the strongest case. Explain the trade-offs and what would change the answer.
Translate the recommendation into priorities, owners, milestones, and measures. Work with the team to deliver and adjust as the evidence changes.
Consider a contractor deciding where to spend its next tranche of capture resources. An opportunity's headline contract value is one input. The decision also depends on win probability, capability gaps, pursuit cost, timing, and revenue exposed to rebid.
A stochastic model compares growth paths across different combinations of wins, losses, and rebids, within the organization's capability and resource constraints. The team can identify the path with the strongest modeled likelihood of meeting the target, then update it as the pipeline and capabilities change.
This is an illustration of the approach, not a forecast or a set of client results.
I'm Chintan Dhanji, founder of SC Strategy Consulting.
I began in actuarial work at EY and Guardrisk, moved into growth strategy at Discovery Vitality, and became a case team leader at Bain & Company. At SC Strategy, I have led growth, acquisition, AI, and financial modeling engagements for large organizations and smaller businesses.
I am an Associate of the Institute & Faculty of Actuaries and hold an MBA from IE Business School. I also completed DeepLearning.AI's Deep Learning Specialization.
A healthcare growth engagement expanded into an enterprise AI strategy after I built a working ML model. A financial services client asked me back for end-to-end cash and liquidity process mapping. The next mandate follows from making the current work useful.
I lead the engagement directly, with specialist support brought in where the scope requires it. We agree the decision, deliverables, and responsibilities before work begins.
View my LinkedIn profileIn a 20 minute conversation, we can discuss the choice, what makes it difficult, and whether my approach would help. You do not need a finished brief to start.