Rx for Compliance: Using Analytics to Manage Life Sciences Risk
Integrating Data Across Compliance and Operations Helps Life Sciences Companies Spot Risk First
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November 02, 2026
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Managing a compliance program can be like driving down the highway. The rearview mirror shows where you’ve been, but it can also divert your attention from what lies ahead.
Compliance programs rightly spend time looking at where they’ve been – auditing historical data, investigating root causes, and analyzing prior results can help life sciences organizations understand past risk events and detect patterns.
But just as avoiding accidents is preferable to cleaning up after them, compliance needs to anticipate risks too. Strategically integrating data analytics into your risk management activities can quickly broaden an organization’s perspective. Rather than reacting to yesterday’s challenges, organizations can proactively monitor operations, anticipate emerging risks and prevent discrete issues from becoming larger problems.
An effective compliance analytics program isn’t simply a dashboard; it’s a management system that connects risk identification to thresholds, accountability and intervention.
Additionally, regulators, auditors, whistleblowers and journalists increasingly use advanced data analytics to uncover patterns and anomalies. Life sciences organizations must do the same to identify and address risks before they become regulatory, financial or reputational events.
Leveraging Data in Risk Management
A well-designed risk management framework can help compliance teams identify key risks, monitor them consistently and establish accountability for responding when something surfaces. Embedding a compliance data strategy into that risk management framework can help to support repeatable processes and drive consistency across the organization.
Organizations routinely collect and use vast quantities of data to drive business performance and inform operational decisions. Compliance teams should have access to the same data, helping align compliance monitoring with core business metrics, and enabling organizations to identify and mitigate risk while advancing strategic objectives.
Identifying Risks
First, compliance and business teams should develop a shared understanding of risk and identify which operational data is maintained across business functions that may reveal risk areas. Access to commercial data, such as sales, shipping or rebating data, and qualitative data, such as physician interactions or patient complaints, can improve how a compliance team identifies and prioritizes risk.
Integrated operational data enables the compliance team to uncover patterns more effectively, prioritize risks and distinguish isolated events from systemic issues. Further, observations derived from data analysis should be supplemented with qualitative inputs. This may include interviews with key stakeholders and business leaders, as well as topics and activities reflecting the focus of regulators and industry trade groups.
Consider the following questions to assess whether your organization is effectively using data to identify risks:
- Do we have a documented, data-driven process for conducting risk assessments?
- Does my annual risk assessment use data to identify risks across regulatory, operational, financial and clinical domains? Have we defined key risk indicators across these domains to monitor and score?
- Are we integrating data from across the enterprise – commercial, financial and compliance – to guide the risk identification?
Monitoring Risks
Once risks are identified and the broader business and compliance teams are aligned, advanced analytics becomes the backbone of risk detection and monitoring.
Most organizations have robust compliance data, but few integrate it with operational and financial data. By incorporating data from broader operational functions, compliance teams can monitor trends using the same information business teams use to run operations. This powerful combination enables compliance teams to establish outlier thresholds and deploy analytics that monitor trends and alert teams when activity diverges from expectations.
For example, commercial risk in health care provider (“HCP”) interactions and speaker activity may remain hidden when relying on expense data alone. Combining travel, meals, event planning, and Salesforce or medical science liaison interaction data provides a fuller view of behavior and helps identify potential risks earlier.
Consider the following questions when developing your monitoring program:
- What data is being gathered to identify patterns and define thresholds?
- Are our monitoring activities generating actionable insights?
- Do we have appropriate thresholds in place to detect data anomalies for our highest-priority risk areas?
Mitigating Risks
When an activity or outcome is identified as a potential compliance risk, data can help to illuminate control gaps that elevated the risk, and inform where new controls might detect or prevent it.
Continuing with the example above, what information is available to leadership teams when identifying an HCP to speak at an upcoming event? Are teams able to see that speaker’s activity level that quarter? That year? Is data available to ensure there are no repeat attendees for substantially similar topics?
With the right data, compliance and business leaders may be able to reduce potential risk. System prompts can be developed to identify when a speaker has participated in too many events. Controls can help ensure any HCP invited to an event has a bona fide educational need. Providing risk indicators to the business can bring them into the compliance process and help mitigate risk before it becomes a bigger problem.
By combining compliance, operational and financial data into an integrated data platform, companies can detect risks sooner, respond more decisively, and strengthen program effectiveness.
Consider the following questions when undertaking a process to mitigate risk:
- Do we analyze data patterns to identify systemic issues and investigate the root causes of risk?
- Is compliance working collaboratively with business units to mitigate identified risks?
- Are we using data to measure the effectiveness of our remediation efforts?
Why You Need a Data-Driven Risk Management Framework
The need for early warning systems reflects the heightened scrutiny of how effectively organizations collect, analyze and act on enterprise data. Regulators and third parties are increasingly using advanced analytics to identify potential misconduct. The question is no longer whether analytics will expose misconduct, but who will find it first.
This shift is evident in the Department of Justice (“DOJ”) Civil Division’s Fraud Oversight through Careful Use of Statistics (“FOCUS”) initiative, which prioritizes collaboration with external data miners who demonstrate “insightful application of sophisticated technological capabilities.”1
The DOJ also created the Health Care Fraud Data Fusion Center (Fusion Center), partnering with the FBI, Office of Inspector General for the U.S. Department of Health and Human Services and other agencies to strengthen data-driven fraud detection and enforcement.2
These efforts are producing returns for the government. As highlighted in the DOJ’s 2026 National Health Care Takedown, which resulted in the charging of 455 defendants in schemes involving more than $6.5 billion in alleged fraud, the operation involved “cutting-edge use of data analytics to target the worst actors.”3
Separately, the DOJ also announced the first prosecution arising from the Fusion Center’s Financial Intelligence Review Team in connection with a $67 million scheme to bill Illinois Medicaid for behavioral health services that were not provided.4
Regulators Are No Longer the Only Watchdog
Regulatory scrutiny is no longer the sole source of compliance risk. A growing range of stakeholders, including competitors, investors and business partners, are leveraging data analytics to detect potential misconduct and evaluate an organization’s compliance posture.
Settlements resulting from the opioid crisis illustrate how data has become an accountability tool for industry partners. Distributors use statistical models and suspicious-order monitoring to identify unusual ordering activity. Once identified, retail pharmacy customers must substantiate dispensing decisions with detailed supporting data. A compliance program without integrated data systems will lose valuable time responding to these requests and may hurt relationships with channel partners.
What Does This Mean for You?
Organizations need to continuously monitor data, identify emerging patterns and intervene before risks escalate. The billions of dollars recovered annually through qui tam settlements and judgments illustrate the cost of allowing regulators, whistleblowers or other third parties to identify risks first.5
The challenge is not simply seeing risks earlier; it is responding to them consistently. That requires a structured framework that identifies and prioritizes issues based on data from multiple stakeholders, assigns accountability, and drives corrective action before problems become enforcement matters. Effective healthcare compliance programs make data central to how they think about and manage risk.
Footnotes:
1: U.S. Department of Justice, Press Release, “Civil Division Announces FOCUS Initiative for Data Miners Filing Qui Tam Complaints” (Apr. 30, 2026).
2: U.S. Department of Justice, Press Release, “Head of the Criminal Division Matthew R. Galeotti Announces Results of Health Care Fraud Takedown” (June 30, 2025).
3: U.S. Department of Justice, Press Release, “National Health Care Fraud Takedown Results in 455 Defendants Charged in Connection with Over $6.5 Billion in Alleged Fraud” (June 23, 2026).
4: Ibid.
5: U.S. Department of Justice, Press Release, “False Claims Act Settlements and Judgments Exceed $6.8B in Fiscal Year 2025” (Jan. 16, 2026).
The views expressed herein are those of the author(s) and not necessarily the views of FTI Consulting, Inc., its management, its subsidiaries, its affiliates, or its other professionals.
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