Compliance automation is the process of using technology and software - such as digitized workflow tools, data analytics and database screening - to bring efficiency to compliance processes and controls.
Automation can replace or support manual tasks such as checks and sampling, validation, and approval processes. Increasingly, compliance automation tools use machine learning and/or AI to improve the way that the system identifies non-compliance.
Compliance rules can be set by external bodies or internal policies, but in all cases a compliance automation system helps organizations - and people.
One specific use case for compliance automation is as part of a suite of risk management tools that supports the identification and investigation of fraud and financial crime within organizations. This might include:
Automation is a central feature of a data analytics system which can - by using specific analyses and weighting them in relation to company spend - provide data-driven spend risk insights.
For example, detection of fraud risk in company spend is traditionally managed on a sample-led basis, where only a small proportion of spend items are assessed for risk against a limited number of risk indicators, and assessed when certain risk criteria are triggered (e.g. value, location, etc).
Automation can transform this process by enabling the analysis of all enterprise spend, all of the time, against a wide range of predetermined risk analyses. By allowing technology to conduct a bulk analysis of all spend data and score the riskiest transactions across multiple indicators, compliance and audit teams can focus their time on identifying those transactions flagged as high-risk (rather than trying to find the proverbial needle in a haystack). .
Automation can be used as part of a due diligence process during onboarding of a third party, and to monitor engagements with that third party across the full lifecycle of the relationship.
From an onboarding perspective, automation can be used to drive escalation and follow-up actions based on a third party’s response to a due diligence questionnaire. It can also support the screening of third parties (sanction lists, watchlists, negative news coverage, etc.) and drive a more detailed risk assessment based on multiple factors.
Automation can then be employed to continuously screen the third party, and monitor transactions with them on an ongoing basis. Engagements can be automatically flagged for further investigation if the terms or parameters of the original engagement criteria change or are breached.
Automation can also help organizations simplify, streamline and strengthen their approvals and disclosure processes - many of which may be managed by way of a manual, paper-based process.
Moving these processes to a digital format paves the way for the introduction of automated processes, such as intelligent question and approval logic that automates decisioning and reduces the need for human involvement within the approval process.
At the same time, automated screening functionality can be used to overlay the process itself. For example, running automated checks against the names of individuals mentioned that might be recipients of company spend can provide deeper context for risk-based decision making.
Data silos and disparate data sources make it impossible to spot patterns manually. A data analytics tool can do this effectively, setting parameters that identify risks of non-compliance using automation.
By looking at a broader range of data, and in real time, it is possible to speed up the identification of risk considerably. The Association of Certified Fraud Examiners (ACFE) estimates that a typical fraud case takes 14 months to be identified, suggesting there is scope for automation to save organizations time and money, and reduce the risk of significant business disruption, reputational damage and regulatory consequences.
An automated compliance system based on data analytics is not a blunt instrument. On the contrary, it can be refined over time to reflect changes in regulation, your business or the environment in which your organization operates. Machine learning can also be leveraged to learn from the resolution of flagged transactions to improve your risk model over time.
Automation reduces the time needed to collect, organize and structure data, allowing your team to focus on interpreting and addressing the risks identified by your compliance technology.
Although the use of automation within compliance, risk management and internal audit is still in its infancy compared to areas such as finance, marketing and sales, it is already helping to transform the way that businesses anticipate, mitigate and tackle compliance risks.
Lextegrity's Integrity Gateway, can satisfy all of the above use cases, enabling organizations to utilize the power of automation in managing third-party risk, approvals and disclosures, and ongoing transaction monitoring.
Find out how Halliburton, Lifescan and Alexion have utilized our technology to automate key areas of their compliance program.