Streamlining Investment with AI-Powered Scoring

Client Overview

Our client is a financial consulting firm focused on high-value investment portfolios for private equity groups and venture capitalists. With over 15 years of experience and a global client base, the client wanted to evolve and scale their investment assessment process. Historically, investment scoring and due diligence were a manual process, relied heavily on subjective evaluations, and required extended timelines, which could result in delays and inconsistencies in the investment process.

In order to keep pace in a competitive financial environment, the client was looking for an AI enabled approach to optimize investment evaluations, provide reliable scoring metrics, and move away from manual analysis altogether. They turned to us to build an enterprise-ready scoring engine using data science and machine learning to analyze investment opportunities in a fast, accurate, and intelligent manner.

Requirements

The client was looking for a smart automated solution that could scale and create a smart and data-driven process to replace traditional vetting of investments. Key features included:

• Create an AI-powered scoring engine to rank potential investments, regardless of the industry and vertical.
• Use both historical data, financial metrics, industry benchmarks and sentiment analysis to determine potential investment opportunities.
• Create a data pipeline that could ingest structured and unstructured data from public and private sources, such as, but not limited to company filings, news feeds, guitarist reports, etc.
• Use machine learning to find the positive indicators of success and the red flags in an investment opportunity.
• Enable predictive scoring based on risk, return on investment potential, scalability, and the founder’s alternative track record.
• Enable model explainability generates to ensure stakeholders understood the rationale for the scores.
• Provide decision makers with real-time dashboards and detailed reporting.
• Integrate with the client’s own CRM and deal pipeline to ensure a seamless data flow.

Challenges

The investment sector contains some embedded complexities that have made the creation and use of an AI based scoring engine very problematic:

• Data Variability and Quality: The input data was in many different formats, including PDFs, spreadsheets, websites, APIs, and third party databases, and all had issues with completeness and accuracy.
• Subjectivity in Evaluation Criteria: Traditional evaluations used human instincts and subjective judgement which do not convert to machine-readable features with ease.
• Changing Risk Factors: Risk factors associated with an investment are constantly being altered by a myriad of changing parameters, including changing market trends, political problems and changes in leadership.
• Regulations: The solution would need to conform with regulation around financial data and privacy while evaluating proprietary investment information.
• Model Accuracy: Another challenge was finding a model that produces high predictive accuracy yet can be explained to investment stakeholders.
• Stakeholder Concerns: Many stakeholders were uncomfortable, or unwilling to transition from their expertise-based investment decision making to AI-based decision making.

Solution Delivered

Our team built a full-service modular AI investment scoring system that has a powerful analytics background.

The components of the solution were as follows:

Data Aggregation Engine: Automated data pipelines were created using APIs, web scrapers, and file parsers to source structured and un-structured data from trusted sources such as Crunchbase, SEC filings, market survey databases, and financial reports.

Feature Engineering: We worked closely with domain experts to convert static investment criteria into mathematical features. Sample features included:

• Prior founding teams exited
• Product-market fit score
• Rate of revenue growth
• Burn rate versus runway
• Industry scalable index
• CAC to LTV ratio

Machine Learning Models: We used ensemble models (Gradient Boosting, Random Forests) as predictive scoring models, trained on historical investment outcomes with cross-validation as a validation to test robustness.

Risk Analysis Module: Included a real-time sentiment analysis using NLP on news articles and social media to highlight potential equality risks to potential reputational risks and emerging trends.

Scoring Engine Dashboard: Built a visual dashboard that provides a score, risk metrics, the important features more heavily influencing the score, and compares the current data to a historical benchmark for that type of deal.

Explainability Layer: SHAP (SHapley Additive exPlanations) was used to provide transparency on how the AI engine was arriving at the investment decisions.

System Integration: The scoring engine was integrated into the client’s deal pipeline system using REST APIs, ensuring the smooth passing of information through the pipeline while causing minimal disruption to existing workflows.

Results

By implementing the AI-powered investment scoring platform the client benefited in the following ways:

1. Rapid Decision Making: The time required for investment evaluation and decision-making was quickened by more than 60%, as the decision making process was faster, allowing go/no-go decisions to be made much faster.

2. Better Predictions: The scoring engine achieved an 87% predictive accuracy rating against historical success data which was greater than legacy scoring based on humans.

3. Uniform Scoring Criteria: Subjective scoring was removed and replaced with a standard scoring model that was transparent and offered stakeholders greater confidence in the process.

4. Data-Backed Investment Approach: The client can now take an investment-backed approach by leveraging real-time investment data and AI analytics, enabling cannon objectives and smarter allocation of capital.

5. Saving Up Man-Hours: Automating the pulling of data and scoring has saved the client hundreds of man-hours of time every month which translates to monetary savings on operational costs.

6. Competitive Advantage: The client is now able to identify viable start-ups earlier in the start-up life cycle, this creates a fastest-moving caliber of company and a competitive advantage over competing VCs (who have slower due diligence).

This machine-driven AI scoring engine has completely changed the way that the client historically has made investments and defined a more intelligent, streamlined, and scalable solution, for what has been an intuitive and manual based process in the past.