Smarter Planning for Law Firms: How Machine Learning is Reshaping Financial Strategy
In today’s changing legal landscape where uncertainty is now the new norm, law firms need to be nimble and forward-thinking when navigating and managing their financial health.
Finance leaders look to Enterprise Performance Management to provide for financial agility to gain a competitive advantage. They are under pressure to deliver faster insights, more accurate forecasts, and strategic guidance that drives growth. Enter the one-two punch of Enterprise Performance Management (EPM) and machine learning (ML): a duo that’s redefining how organizations plan, analyze, and act..
Why Mash Up EPM and Machine Learning?
EPM is a robust platform that supports best-in-class financial planning, budgeting, forecasting, and reporting. Layer in machine learning and you unlock predictive capabilities that goes beyond traditional analysis:
- Predictive Forecasting: ML models can analyze historical data and external signals to generate forecasts that adapt in real time.
- Anomaly Detection: Spot outliers in financial data before they become costly errors.
- Driver-Based Planning: ML helps identify the true drivers of performance, enabling more accurate what-if scenarios.
- Continuous Learning: Unlike static models, ML algorithms improve over time, refining predictions as new data flows in.
Use Cases for the Legal Industry
One of the most common questions heard throughout finance organizations is “WHY” employ ML with EPM. What are the use cases for leveraging ML and how can it be used to plan better than traditional methods.
Some examples of how forward-thinking organizations are using ML to improve their forecasting methods include.
- Revenue Forecasting: Predict client billings, forecast potential revenue opportunities.
- Expense Planning: Detect unusual spending patterns and optimize cost allocations.
- Workforce Planning: Forecast attrition and hiring needs based on historical and market data.
- Scenario Modeling: Simulate macroeconomic impacts using dynamic, data-driven assumptions.
A Strategic Approach to Kick-Start Your ML Journey
Getting started is often simpler than it seems. The real challenge lies in securing the right datasets to feed into your models to achieve the desired outcomes.
Here are some steps to include in your roadmap if you’re considering integrating ML into your EPM strategy:
- Start with High-Impact Areas: Focus on forecasts or processes that are time-consuming or error-prone.
- Leverage Built-in Tools: Use predictive planning features before investing in custom models.
- Build Cross-Functional Teams: Finance, IT, and data science must collaborate to align models with business goals.
- Monitor and Iterate: ML is not a “set it and forget it” solution—track performance and refine regularly.
