For finance teams operating at high transaction volumes, identifying an unusual transaction is only the beginning. The harder questions come next: Does it represent a meaningful financial risk? How widespread is the issue? Who needs to investigate it? And how does the accounting team turn that finding into action without slowing the close?
In this Vision 2026 customer session, Fernando Cortés, Manager of Revenue Accounting at Turo, joins Michelle Wong, Director of Value Architecture at MindBridge, and Dana Williams, Principal Product Marketing Manager at MindBridge, to explain how Turo has evolved its approach to financial oversight as the business has grown.
Turo operates the world’s largest car-sharing marketplace, connecting guests with vehicles hosted by people in communities around the world. Fernando explains that Turo’s revenue accounting team consists of just five accountants responsible for recording activity across the transaction lifecycle, from an initial vehicle booking through subsequent modifications and adjustments.
That team operates against an aggressive close timeline of approximately business day five while processing millions of transactions each month. Turo also operates across five countries, adding further complexity to the financial environment.
Before MindBridge, identifying potential issues was largely reactive. Some issues surfaced through Turo’s annual audit and quarterly testing with KPMG. Others emerged through engineering channels when product bugs or edge cases were identified. In either case, the accounting team would return to the underlying data, determine the scope of the problem, identify affected transactions, and work through remediation.
The challenge was that the team often had to wait for an issue to surface before it knew what to investigate.
Turo wanted to move toward a more proactive model.
Today, Turo’s data engineering team has built a pipeline that feeds month-end data into MindBridge on business day one of the close. While the accounting team works through journal entries, reconciliations, flux analysis, variance analysis, and other close activities, MindBridge analyzes the transaction data. The team then incorporates review of the resulting risk signals into its month-end process.
Each member of the five-person accounting team can take responsibility for a specific analysis, review the findings, and determine whether something requires further investigation or action. Depending on the issue, that can mean working with engineering on a product bug, coordinating with customer service, communicating an adjustment to FP&A, or determining that the activity has a valid business explanation.
Fernando emphasizes that business context is essential. An unusual transaction is not automatically a problem. The accounting team needs to understand why a transaction sits outside expected patterns and whether it indicates a breakdown in an established workflow or simply reflects legitimate activity.
That context was also important during implementation. Turo worked with MindBridge to explain its data, transaction flows, previously identified issues, and areas where the accounting team wanted greater visibility. That process helped establish control points aligned with Turo’s business and gave the team a stronger starting point for distinguishing meaningful risk signals from activity that did not require action.
The process continues to evolve. When Turo determines that a finding is explainable or that a control point needs adjustment, the team provides that context back to MindBridge. Fernando describes this as an ongoing feedback loop that helps reduce false positives and keep the analysis aligned with changes in Turo’s business.
One example illustrates how the process works in practice.
Within roughly two months of implementing MindBridge, Turo identified a group of transactions falling outside the expected profit-margin range for its two-sided marketplace. The accounting team investigated the characteristics of those transactions and worked with engineering to trace the issue to an edge-case bug introduced through a recent product release.
The issue was not material at the time it was identified, but finding it early allowed Turo to understand the affected population and take action before the issue became more significant within the quarter.
Engineering fixed the underlying bug so future transactions would not be affected. Turo also added rules within its revenue recognition system to flag similar cases if they appeared again.
Fernando describes this combination as particularly valuable. Rules can help prevent a known issue from recurring, while broader analysis can surface unknown unknowns, meaning emerging risks and patterns that no predefined rule or threshold was designed to catch.
The finding also demonstrates why financial oversight increasingly crosses organizational boundaries. Accounting had to translate the financial issue into terms engineering could act on, communicate potential impacts to FP&A, understand the affected population, and consider the experience of Turo’s guests and hosts.
Michelle explains that full-population analysis is one part of that process. MindBridge can analyze 100% of the transactions in the population, apply multiple control points to identify and rank risk, and explain why particular transactions were flagged. That gives accounting teams a more focused starting point for investigation, but professional judgment remains necessary to interpret what a signal means in the context of the business.
The session then looks toward Turo’s next stage of financial oversight.
Fernando describes the potential to connect MindBridge findings with enterprise AI tools and other systems used across Turo. Instead of accountants manually moving between MindBridge, Slack, product information, engineering tickets, and other sources during an investigation, connected workflows could gather relevant context, help prioritize findings, and route follow-up work to the appropriate teams.
The objective is to reduce administrative work around investigation and coordination so accountants can spend more time interpreting what the numbers mean and deciding what deserves action.
Both Fernando and Michelle emphasize that human judgment remains central to this process. Technology can process large transaction populations and focus attention on higher-risk activity, but Turo’s accountants remain responsible for financial integrity and for deciding which findings should be escalated to other teams.
For finance organizations beginning a similar journey, Fernando and Michelle recommend starting with a focused problem rather than trying to address every possible use case at once. Identify data sets where issues have occurred before, establish business context during implementation, validate the findings, and maintain open communication about what is and is not useful.
Looking further ahead, Fernando discusses the possibility of moving financial oversight closer to the daily flow of transactions rather than waiting until month-end. For a business processing millions of transactions, earlier identification and investigation could allow the accounting team to resolve more issues before the close and devote more of its time to strategic work.
Turo’s experience demonstrates that financial oversight at scale is not simply about finding more unusual transactions. It is about creating a repeatable process for identifying what matters, understanding why it matters, coordinating the right response, and learning from each finding so the process becomes more focused over time.
Chapters
01:11 Welcome and introduction to Turo
04:29 Managing millions of transactions with a five-person accounting team
06:29 How Turo identified financial issues before MindBridge
11:25 Turo’s financial oversight process today
13:58 Deciding which risk signals deserve investigation
18:48 Refining findings as the business changes
21:37 Customer example: detecting an edge-case product bug
24:17 Turning a financial finding into cross-functional action
26:49 From reactive rules to proactive risk detection
29:55 Full-population analysis, risk scoring, and explainability
33:48 How Turo’s oversight process has evolved
36:14 Connecting financial findings with AI and other business systems
37:54 Where human judgment adds the most value
41:07 Practical lessons for finance teams getting started
46:09 The future of Turo’s financial oversight and Q&A
