Crafting a Proactive Approach
Volume 1 of this report discussed the measures that organizations are taking to prevent and detect issues such as bribery and corruption, including proactive data analytics and enterprise-wide risk assessments. However, as illustrated by our survey, 82% of respondents are still feeling the significant impacts of fraud or other misconduct. This suggests organizations are not harnessing the true potential of proactive data analytics to mitigate risk.
Data analytics is a broadly used term, with many firms only scratching the surface of its true potential. Given the range of data, tools and techniques available, a key component of leveraging data analytics effectively is to take a holistic approach to data governance. Organizations that plan ahead and get their arms around where their critical data resides are in a far stronger position when faced with an investigation.
Excellence and Efficiency Through Technology
Organizations can achieve effective, efficient risk mitigation strategies through the innovative use of technology and analytics. Applying meaningful data insights to a situation can significantly enhance decision-making and reduce cost throughout the compliance, investigation and litigation life cycle.
The way an organization collects and reviews data is particularly important. Good data governance not only enables an organization to hit the ground running when planning an internal investigation, but also reduces costs associated with compliance, investigation and downstream litigation. Beyond this, it also reduces internal costs associated with hardware for storing unnecessary data on servers that should be deleted under a retention policy, as well as staff to manage the data and servers.
When databases are maintained properly and processes become more automated, the time needed for preparation before an investigation will decrease significantly, thus allowing investigators more time to focus on finding the facts that matter.
Over time, tools such as AI models can start enriching decisions. Data can be purged after each investigation, with models retained for future investigations. Data visualization tools can also be developed over time to save time and cost. For example, creating dashboards for different audiences can be a constant work in progress, incorporating feedback from the people who use them to refine and improve the usefulness of the data visualizations. If they are properly set up, the amount of time needed for data preparation and implementing changes will decrease and decision-makers will have the information they need at their fingertips.
