Audit trail
Dataiku's built-in audit trail logs all actions performed by users, allowing for advanced monitoring and simplifying compliance constraints.
Learn MoreWe recently sat down with Thomas Kampioni, Director of IT at The Law Society of British Columbia to understand their key data and analytics use cases, the role Dataiku plays in helping them solve critical business problems, and the tangible value they have achieved since they started using Dataiku.
The senior management team at The Law Society of BC firmly believes that AI and machine learning will play an important role in their responsibilities in the near future. They knew it was time to take advantage of their collected data and leverage technology to identify patterns and behaviors and increase effectiveness and efficiencies within Law Society programs.
The first step in any AI or machine learning project is to define the business problem that needs to be solved. In the case of the Law Society, it was to increase the efficacy of the trust assurance audit program. The organization regulates 3,800 law firms and audits approximately 550 firms per year, which means that each firm is audited at least every four to six years. Predictive analytics and a risk-driven audit schedule allowed the team to focus their audit efforts on those firms that present higher risks in relation to the volume and type of trust transactions.
The Law Society has three decades of historical data, which enables them to categorize law firms according to their risk level: low, neutral, or high risk. The organization made the decision to focus on risk factors and, from there, work to adjust the audit schedule based on the risk category of each firm. To approach this business problem, the team knew they had to:
“The partnership with Dataiku helped us get insights into our own data that we have been collecting for decades. Thanks to Dataiku, we were able to understand which factors are correlated with certain outcomes as well as predict future events.”Thomas Kampioni Director of IT
The Law Society of BC evaluated a myriad of data science and machine learning platforms and ultimately chose Dataiku. The Law Society uses Dataiku to support proactive regulation of law firms. The trust audit program has the overall goal of being an effective and efficient program that helps ensure lawyers handle trust funds appropriately, and Dataiku helps identify risk factors for law firms.
The Law Society uses predictive analytics in Dataiku to understand which factors contribute to a firm being a risk, such as years in business, the lawyers’ average years of practice, the number of complaints and hearings, the last trust audit score, and so on.
Using Dataiku, they predict the probability of a firm’s risk from a compliance perspective, identifying the firms with high, low, and neutral risk factors according to their background and history. Upon identifying firms that fall under the high risk category, the Law Society then asks the firm for books and records to detect and analyze anomalies further using audit procedures and algorithms.
For The Law Society of BC, who currently use Dataiku’s free edition, the most appealing thing about Dataiku is the end-to-end aspect, enabling them to complete an evaluation, run a project, and share insights with executives via data visualizations within the platform. This came in handy during their proof of concept with Dataiku, demonstrating the value of the solution to a broad range of users. Further, they like how easy the tool is to use — you don’t need experience with traditional data science programming languages like Python or R to jumpstart data efforts, there’s a strong selection of on-the-shelf algorithms and visualization tools, and it’s easy to connect internal data sources.
Thanks to Dataiku (and a robust analytics strategy), The Law Society of BC was able to:
"Data is a commodity more expensive than gold.”Thomas Kampioni Director of IT
Customers building predictive analytics solutions with Dataiku benefit from:
See how Envelop Risk took a holistic approach to characterising the cyber risk economy, deploying dozens of machine learning models to predict behaviour, incentives, and diffusion, in order to build the next generation of insurance products.
Read moreDataiku's built-in audit trail logs all actions performed by users, allowing for advanced monitoring and simplifying compliance constraints.
Learn MoreSee how BGL BNP Paribas was able to improve fraud detection and democratize the use of data across the organization while maintaining their high standards for security and data governance.
Learn MoreSee how one health insurance company was able to implement a machine learning-based fraud detection system that is 3x more effective.
Learn MoreArmed with Dataiku, Orange was able to start transitioning smaller BI projects to the business and work on machine learning use cases like call load detection and triage, a model that took less than a month for the team to build using Dataiku.
Learn More