AI in Sports Betting Finance: Faster Close, Smarter Ops

U.S. sports betting companies don’t lose sleep over models—they lose sleep over messy operations. Month-end close collides with promo cycles, data lives in too many systems, and every “quick question” from leadership turns into an afternoon of spreadsheet archaeology.

Fanatics Betting and Gaming’s finance team put a simple stake in the ground: use AI to cut the manual work so people can spend more time on decisions. Andrea Ellis, CFO at Fanatics Betting and Gaming, described it plainly—AI helps teams stop hunting for information and start answering “So what are we going to do about it?” That mindset sits right at the center of this AI in Sports Betting: Odds and Analytics series: better automation, faster insight, and tighter execution across digital services.

This post breaks down what Fanatics did, why it worked, and what other U.S. digital service and gaming operators can copy—especially if you’re trying to improve sports betting operations, accelerate financial close automation, and make risk and performance reporting more reliable.

The real bottleneck in sports betting ops isn’t math—it’s manual work

Answer first: In sports wagering, teams already have plenty of analytics; the bottleneck is turning dispersed data into consistent, decision-ready reporting.

Sports betting is a high-frequency business. Pricing, promos, liability, payments, and customer behavior shift daily—sometimes hourly. Finance and analytics teams end up doing the same chores repeatedly:

• Pulling data from multiple sources (payments, CRM, trading/risk, marketing, customer support)
• Reconciling definitions (“active user” vs “bettor” vs “depositor”)
• Summarizing vendor contracts, invoices, and approval trails
• Producing executive-ready narratives after the numbers are already “done”

Ellis frames the modern CFO role as operational and cross-functional, not just a reporting function. That’s especially true in U.S. digital services like online sports betting, where finance has to connect product, risk, marketing efficiency, and customer retention—fast.

Why this matters more during peak seasons

Late December is a good example. Between NFL playoff pushes, bowl season, NBA holiday slates, and aggressive promotional calendars, the end-of-year reporting cycle can be brutal. If your team is buried in manual close tasks, you’re slower to:

• Adjust promo spend based on payback windows
• Catch margin compression early
• Spot cohort quality changes (bonus abuse, low-LTV acquisition)
• Flag vendor overages or contract risks

AI doesn’t replace finance judgment. It buys time for it.

Fanatics’ approach: go deep first, then scale adoption

Answer first: Fanatics avoided “AI everywhere” and instead targeted a few high-impact workflows, then built a repeatable adoption system.

A common failure mode in enterprise AI adoption is scattering effort across dozens of use cases. You get demos, pilots, and enthusiasm—without measurable operational change. Fanatics took a different path: land meaningful wins in key areas (finance and customer operations), then broaden.

That sequencing matters. In sports betting analytics, the most valuable improvements usually come from fixing repeatable workflows:

• Monthly close
• Vendor and contract review
• Forecast refresh cycles
• Board/executive reporting
• Cohort and promo analyses

Ellis’ “big picture” point is subtle but important: if AI only speeds up tasks, you’ll just do more tasks. The goal is to shift the team’s time from execution to strategy.

The adoption playbook: treat AI like a daily tool, not a side project

Fanatics focused on making ChatGPT a “daily partner,” not a special occasion. Their structured rollout included:

1. An AI automation task force to keep work prioritized
2. Process inventory from the finance team (what’s manual, repetitive, error-prone)
3. A roadmap of projects, rather than ad hoc experimentation
4. Baseline training so everyone can use the tool safely and consistently
5. A “GPT-athon” (a build day with data scientists) to create custom GPTs for specific workflows
6. Ongoing updates and recognition in all-hands to keep momentum

If you’re leading AI in a sportsbook, this is the part to copy: adoption is a management system. Tools don’t implement themselves.

A concrete win: VendorID GPT and the hidden value of “18 hours saved”

Answer first: Automating vendor identification and contract summarization saved Fanatics about 18 hours per month, which compounds into faster close and fewer errors.

Fanatics built a custom tool (“VendorID GPT”) to automate vendor identification and summarize contracts—work that typically involves searching documents, reading clauses, and cross-referencing systems.

Saving 18 hours monthly may sound modest until you map it to betting operations reality:

• Those hours are usually concentrated during close, when teams are already overloaded.
• Less time spent on document wrangling means fewer rushed decisions.
• Faster access to vendor terms reduces approval bottlenecks and surprises.

I’ve found that “time saved” only becomes “value created” if you reinvest it intentionally. Fanatics’ framing suggests they did: reclaim time to analyze performance, communicate faster, and run better executive discussions.

How to find your own “VendorID GPT” equivalent

Look for workflows with three properties:

• High repetition: the task happens weekly or monthly
• Text-heavy inputs: contracts, invoices, policies, ticket notes, emails
• Clear output format: summaries, categorizations, exceptions, approvals

In sports betting, that often includes:

• Payment processor statements and exception logs
• Responsible gaming policy documentation and case notes
• KYC/AML escalation summaries (with strict controls)
• Marketing IOs, affiliate terms, and bonus eligibility rules

The easiest early wins aren’t the fanciest models. They’re the ones that eliminate “copy/paste and interpret.”

From faster reporting to better decisions: AI for sports betting analytics

Answer first: The biggest operational upside is faster insight cycles—AI shortens the time from data to decision in trading, marketing, and finance.

Ellis highlighted that AI helps them get information faster, reduce close time, and analyze trends or customer cohorts more dynamically. That lines up with where AI in sports betting analytics is heading: speed and clarity.

Here are three practical areas where that matters.

1) Cohort analysis that operators actually use

Cohort work often dies in dashboards because it’s hard to interpret quickly. AI can help by:

• Generating plain-English explanations of week-over-week cohort shifts
• Highlighting statistically meaningful changes (not noise)
• Producing “what changed and why it matters” summaries for exec updates

For example: “November paid media cohorts are depositing at similar rates, but second-week retention dropped after promo tightening. Net gaming revenue per user is flat; payback window extends by 9 days.” That’s a decision-ready statement.

2) Scenario analysis that doesn’t take two weeks

Fanatics’ roadmap includes using generative AI as a thought partner for planning and scenario analysis. In a sportsbook context, scenario analysis shows up everywhere:

• Promo changes (deposit match vs bet-and-get)
• Risk tolerance adjustments
• State launches and expected ramp curves
• Vendor pricing changes

AI helps by speeding up the mechanics—building a scenario template, documenting assumptions, comparing versions, and turning it into an exec narrative.

3) Executive communication: the underrated use case

CFOs spend an enormous amount of time translating performance into a story leadership can act on. AI can draft:

• Monthly business reviews (MBRs)
• Variance explanations (budget vs actual)
• Board-level summaries of risk, margin, and customer health

The standard here should be strict: outputs must be traceable to approved data sources. But when done right, this is one of the highest-ROI applications.

Guardrails that matter in regulated U.S. sports betting

Answer first: AI in sports wagering needs tight governance—data handling, auditability, and responsible gaming controls aren’t optional.

Sports betting is regulated state by state, and operator trust is fragile. If you’re using AI for finance and operations, you need guardrails from day one.

Practical governance checklist for AI in betting operations

• Data access control: restrict what the model can see; segregate sensitive customer data
• Approved sources only: connect AI workflows to sanctioned datasets and document repositories
• Human approval for consequential outputs: anything affecting payouts, compliance decisions, or accounting entries needs review
• Audit trails: keep logs of prompts, outputs, and who used what (especially for finance close)
• Responsible gaming alignment: if AI touches customer communications, embed RG rules and escalation paths

A good internal standard is: if you can’t explain how an AI output was produced and validated, it doesn’t belong in close, compliance, or customer risk decisions.

What other U.S. digital service teams can copy next week

Answer first: Start with one workflow, measure time saved, and formalize adoption so it doesn’t fade after a pilot.

If you’re operating in sports betting, iGaming, fintech-adjacent digital services, or any high-volume marketplace, here’s a pragmatic rollout plan modeled on what worked at Fanatics.

Step 1: Pick one “close pain” and define success

Choose a workflow like vendor summaries, variance explanations, or recurring reconciliations.

Define success with a number:

• Hours saved per close cycle
• Reduction in rework (fewer corrections after review)
• Shorter time to executive-ready reporting

Step 2: Standardize prompts and outputs

Don’t let every analyst invent their own method. Create:

• A prompt template
• A required output format (bullets, table, summary + exceptions)
• A “what not to do” list (no guessing, no unsupported numbers)

Step 3: Hold a build day

Fanatics’ GPT-athon idea is strong. One day of focused building beats six weeks of sporadic tinkering.

Bring finance, analytics, and security/compliance together and ship one usable tool.

Step 4: Make it visible and routine

Adoption sticks when leaders ask for the AI-supported output in recurring meetings. If nobody requests it, it becomes optional—and optional tools die.

Where AI in sports betting is heading in 2026

Fanatics’ story points to a bigger shift in this series: AI is moving from analytics as a dashboard to analytics as a working partner. The winners won’t be the teams with the most models. They’ll be the teams that reduce cycle time—idea to analysis to decision—without compromising governance.

If your finance team can close faster, your marketing team can adjust spend faster, and your risk team can communicate exposure faster, you’ve built an operational advantage that compounds.

The question worth asking as you plan next quarter: which decisions are you making too late because the team is stuck assembling information?