From Hype to Help: Practical AI for Actuarial Workflows

By: Mahima Khandelwal

1. Introduction – The Changing Tide

Picture this: you’re in the middle of quarter-end chaos, emails pinging, numbers shifting, and your report deadline looming. You press the big imaginary “Generate my report” button: the tool asks GenAI a few targeted questions about the numbers, writes a board-ready paragraph, and inserts it (charts and all) straight into your report. That’s the power of GenAI. Think of it as a witty, caffeinated intern, except this intern never sleeps, never forgets, and is powered by APIs and automation that handle the grunt work in the background.

For actuaries, whose world is full of models, documentation, and regulatory reporting cycles, this isn’t a distant dream, it’s already starting to happen. But as exciting as it sounds, one truth runs through everything: speed without trust is meaningless. Governance isn’t an afterthought; it’s the foundation that allows these tools to scale responsibly.

So, the real question isn’t “Will AI impact actuarial work?” but “How quickly can we adapt, and how do we build trust as we do so?”

2. Speaking Plainly: LLMs, APIs and Why They Matter

At its core, a Large Language Model (LLM) is just a super-powered text predictor. It takes in words (your prompt) and predicts the next ones with impressive fluency. The magic emerges when paired with APIs, which allow it to communicate with your existing systems.

Here’s the simple picture:

• You: Ask a question (“Summarize reserve movement for the quarter”).
• API (the connector): Pulls the right data, adds context (documents, charts, retrieved snippets), enforces security and logging, and calls the LLM.
• LLM: Writes the summary, tailored to your audience.

 

That’s the core pattern. It’s not about replacing actuarial judgments; it’s about giving actuaries a power tool that connects what they already have to what they need faster.

3. The Story of Anita – A Hybrid Actuarial Workflow

How Anita Turns a Vague Ask into Board-Ready Scenarios

Anita, a valuation actuary, types a simple instruction:
“Generate 200 plausible 20-year scenarios with a flu-like mortality wave and a two-year lapse spike after an employer closure.”

Behind the scenes, the system calls the LLM via an API and returns neat scenario headlines, a one-line rationale, and numeric parameters the valuation engine can plug in. What Anita sees looks like the first draft from a sharp analyst, done in minutes.

Refine the Ask – Prompt Engineering

Her first run taught Anita a blunt lesson: how you ask matters. “Make extreme scenarios” produced vague, hard-to-use stories. When she changed the brief to: “Return a title, a one-line rationale, and three plug-in numbers — mortality multiplier, lapse multiplier, and a one-line interest path” the outputs became precise and actionable.

Prompting isn’t a trick — it’s the difference between a usable draft and noisy output you must clean up. Better prompts give you consistency, speed up review, reduce hallucinations, and make results reproducible; poor prompts deliver—quite literally—garbage in, garbage out.
Quick tips Anita follow: specify the exact format, show a tiny example, list required fields, and version your prompt templates so they’re auditable.

Retrieval-Augmented Generation: Making AI Speak Our Language

When Anita asks for a scenario that reflects an employer-closure lapse spike, the system doesn’t guess corporate realities, it looks them up. Behind the scenes, relevant company documents (policy wording, reinsurance treaties, internal mortality studies) are converted into embeddings and stored in a vector database. The RAG pattern then retrieves the most relevant snippets and supplies them to the LLM as context, so each scenario can reference the exact clause or study that motivated it.

For Anita, that means the narratives aren’t generic: they cite firm evidence, reduce hallucination risk, and make the scenarios easier to defend to auditors and management.

Chain the Work – Step by Step

Anita knows from experience that one big request such as “Generate scenarios, give me the numbers, and suggest hedges,” often produces a jumble—some parts are useful, while others are unusable. Instead, she breaks it down. First, the model creates clear scenario stories. Next, it extracts the exact multipliers her valuation engine needs. Finally, it proposes a shortlist of hedge ideas with plain-English rationales. By chaining the steps, Anita turns one messy black box into a series of clean, reviewable outputs. Each step can be checked and corrected before proceeding, making the process faster, more accurate, and far more reliable.

Human Review & Sign-Off

Finally, Anita’s team samples outputs, checks for coherence (no negative interest rates, no unrealistic mortality spikes), and signs off. AI drafts, compares, and summarizes like a pro — but Anita still applies her actuarial expertise to check assumptions and ensure the model’s integrity. That human-AI partnership is the sweet spot.

It sounds complex, but in practice, it feels like magic, because all Anita sees is a button that drafts her first scenarios for her.

Why Automate?

AI can crunch volumes of data in seconds—but it won’t catch a regulatory nuance or interpret a mortality trend without Anita’s judgment. That’s why she automates: not to replace expertise, but to make answers usable, repeatable, and defensible. Her pipeline builds a searchable scenario catalogue, stories, plug-in numbers, and the supporting snippets, so results can be re-run, compared, and audited with a clear provenance trail.

Automation also unlocks scale. Instead of 10 tests, Anita can run 200, surface the few that matter, and iterate with treasury the same day. Errors shrink, reviews speed up, and actuaries focus on judgment, not formatting. Guardrails like prompt templates and plausibility checks keep outputs consistent and safe. In short: automation doesn’t replace judgment—it amplifies it at scale.

4. Enter Agents – The Next Leap

But what if Anita didn’t even need to push the button herself? This is where agentic AI comes in. Agents don’t just respond—they plan, take multiple steps, and call tools autonomously until they achieve a goal.

For example, an agent could:

1. Generate dozens of mortalities, lapse, and interest scenarios.
2. Run them through a valuation engine.
3. Flag scenarios, where solvency ratios breach thresholds.
4. Suggest hedging strategies.

All before handing results back to the actuary for review. It’s powerful—but autonomy raises the stakes. That’s why governance must stay at the centre: every prompt, retrieval, tool call, and output must be logged and explainable. In actuarial work, transparency isn’t optional—it’s the license to operate.

5. Business Value – Why This Matters

So, what’s the payoff? Better, faster, more informed decisions.

• Scenario Testing: Move beyond a handful of deterministic shocks, AI can generate hundreds of plausible futures, giving deeper insight into vulnerabilities.
• Decision Support: For pricing committees, AI assistants can summarize options, stress-test them, and present trade-offs in risk and return.
• Operational Efficiency: Drafting reports, preparing decks, summarizing assumptions, tasks that once drained hours can now take minutes.

The net effect is that actuaries shift time from formatting and manual searching to higher-value judgment and communication.

6. Trusted by Design – Why Governance Comes First
   
   

Still, speed without trust isn’t progress. For all the promise of LLMs and agents, governance isn’t optional — it’s the foundation. Actuarial work has always demanded auditability and transparency, and AI is no exception.

Anita knows this firsthand. Each scenario she generates leaves a trail: the exact prompt she typed, the company documents retrieved, the intermediate steps the system ran, and the final outputs. Later, when her CRO asks how a particular stress was constructed, Anita doesn’t shrug or rely on memory, she pulls up the log. Every step is documented, reproducible, and explainable.

It feels less like a mysterious black box and more like an extended control cycle, the same rigor actuaries apply to valuation models, applied to AI workflows. And this rigor pays off. Once stakeholders see the clear trail from input to output, the conversation shifts. It’s no longer “Can we trust this?” but “How do we scale this safely?” Governance, in Anita’s world, isn’t a bureaucratic afterthought it’s the enabler of confidence, adoption, and eventually scale.

7. Addressing the Hesitations

AI is brilliant at patterns, but it’s not infallible — actuarial judgment is still essential for nuance, complex assumptions, and catching numerical slip-ups. Common questions include:

• Data Privacy: Sensitive policyholder data can’t be allowed to leak. APIs often route queries through external servers—even if vendors say traffic isn’t used for training, exposure risk remains. The fixes include keeping LLMs inside a private cloud, running API gateways with redaction, and using synthetic data that mimics real patterns without revealing personal details. Wrapped in a compliance framework—encryption, audits, and privacy reviews—these guardrails ensure speed doesn’t come at the cost of trust.
• Validation: Can we trust outputs from a “black box”? Treat AI like any new model test, document, and validate. The control cycle you already use applies here, too.
• Job Impact: Will AI replace actuaries? Not likely. It automates grunt work; actuaries stay in the driver’s seat for interpretation, governance, and communication.
• Hype vs. Reality: Many pilots fail because they chase flash over value. The key is to start small, focus on real workflow pain points, and scale gradually.

8. Wrapping Up – Embracing the Shift

We’re at a crossroads. AI tools like LLMs and agents aren’t science fiction anymore—they’re here and moving fast. For actuaries, this isn’t about replacing rigor; it’s about extending it. Automate where it makes sense, validate where it matters, and keep human judgment at the core.

Trust by design isn’t the finish line—it’s the launchpad. Once workflows are explainable, auditable, and reliable, speed doesn’t come at the expense of integrity.

So when quarter-end chaos looms, don’t just grind through it. With the right blend of LLMs, APIs, and agents, you can transform noise into insight. Not just faster but smarter, sharper, and with confidence you can sign.

Note: The author Mahima Khandelwal is a Senior Consultant at EY, India