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:
- Bring in a data science and machine learning platform to help analyze the troves of data they have (which will continue to grow as their digital footprint does) and reduce the risk of human error.
- Modernize their data and analytics program to easily obtain insights into their own data, uncover trends and patterns, and solve business problems.
- Spend time properly defining the business objective in a SMART way (Specific, Measurable, Achievable, Relevant, and Time-Bound). This way, everyone on the team knows what they are trying to accomplish and, in the end, can assess if the project is successful.
- Form a group of motivated, relevant stakeholders, which included subject matter experts, data scientists, and a project manager.
- Iterate with the group to determine the factors for high-risk firms such as time in business, how much money they handle, and so on.