Dataiku Case Studies How The Law Society of BC Uses Dataiku for Risk Ranking and Anomaly Detection

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	How The Law Society of BC Uses Dataiku for Risk Ranking and Anomaly Detection Dataiku

How The Law Society of BC Uses Dataiku for Risk Ranking and Anomaly Detection

Dataiku

Technology Category	Analytics & Modeling - Machine Learning Analytics & Modeling - Predictive Analytics
Applicable Industries	Professional Service
Applicable Functions	Business Operation
Use Cases	Predictive Quality Analytics Regulatory Compliance Monitoring
Services	Data Science Services
Challenge	The Law Society of British Columbia, a non-profit organization that regulates lawyers in British Columbia, was looking to increase the efficacy of their 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. 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. 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. Read More
About Customer	The Law Society of British Columbia is a non-for-profit organization that regulates lawyers in British Columbia with the mandate to protect the public interest in the administration of law and to ensure independence and competence of lawyers. They also bring a voice to issues affecting the justice system and the delivery of legal services. The Law Society of BC regulates 13,000 practicing lawyers and 3,800 law firms. They cover the end to end law lifecycle, from regulation initiatives to access to justice. Read More
Solution	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. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Machine Learning

Analytics & Modeling - Predictive Analytics

Applicable Industries

Professional Service

Applicable Functions

Business Operation

Use Cases

Predictive Quality Analytics

Regulatory Compliance Monitoring

Services

Data Science Services

Challenge

The Law Society of British Columbia, a non-profit organization that regulates lawyers in British Columbia, was looking to increase the efficacy of their 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. 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. 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.

About Customer

The Law Society of British Columbia is a non-for-profit organization that regulates lawyers in British Columbia with the mandate to protect the public interest in the administration of law and to ensure independence and competence of lawyers. They also bring a voice to issues affecting the justice system and the delivery of legal services. The Law Society of BC regulates 13,000 practicing lawyers and 3,800 law firms. They cover the end to end law lifecycle, from regulation initiatives to access to justice.

Solution

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.

Impact #1	Improve the efficiency of their trust audit program, as analyzing large datasets can now be done in seconds to highlight the abnormalities that require auditor attention
Impact #2	Reduce the number of auditors required per one large audit by 14% and the time needed to complete a large audit by 29%.
Impact #3	Leverage predictive analytics and a risk-driven audit schedule to help prioritize audits

Impact #1

Improve the efficiency of their trust audit program, as analyzing large datasets can now be done in seconds to highlight the abnormalities that require auditor attention

Impact #2

Reduce the number of auditors required per one large audit by 14% and the time needed to complete a large audit by 29%.

Impact #3

Leverage predictive analytics and a risk-driven audit schedule to help prioritize audits

Benefit #1	Reduced the time needed to complete a large audit by 29%
Benefit #2	Reduced the number of auditors required per one large audit by 14%
Benefit #3	Enabled the organization to analyze large datasets in seconds

Benefit #1

Reduced the time needed to complete a large audit by 29%

Benefit #2

Reduced the number of auditors required per one large audit by 14%

Benefit #3

Enabled the organization to analyze large datasets in seconds

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Overview

How The Law Society of BC Uses Dataiku for Risk Ranking and Anomaly Detection

Operational Impact

Quantitative Benefit