Case Studies

Equity investments serve as the primary engine for wealth creation, providing a reliable venue for the majority of retail investors to park their capital and participate in global market growth. However, the inherent volatility of these assets often makes it difficult for individual investors to separate meaningful signals from temporary market fluctuations. At Bluepoint & Co, we developed a proprietary model designed to bring institutional-grade clarity to the retail space, transforming historical data into actionable foresight. By applying this framework to Alphabet Inc. (GOOGL), we successfully identified a 180-day price target while simultaneously mapping the statistical boundaries of risk through expanding confidence intervals.

Our approach bridges the gap between fundamental valuation and systematic execution, ensuring that capital is not just parked, but protected. While our predictive analytics suggested a 3.95% upside, our model integrated core metrics—such as a 32.37 P/E ratio—to maintain a realistic perspective on current market pricing. To mitigate the impact of daily "noise," which our system calculated at ±$8.07, we implemented a disciplined risk management protocol. By adhering to a strict 1:3 risk-to-reward ratio and establishing a firm stop-loss, Bluepoint & Co provides a structured methodology that replaces emotional trading with data-driven confidence.

The analytics provided by Bluepoint & Co are for informational purposes only and do not constitute professional financial advice. All investments carry risk, and past performance or model projections are not a guarantee of future market results. Please consult with a licensed professional before making any investment decisions.

At a financial services firm, a "New Money Bonus" digital marketing campaign successfully drove rapid customer acquisition but created a critical disconnect with risk ownership. This "growth at all costs" approach led to a concerning spike in fraudulent activity, resulting in a $12.8M year-to-date loss and a 12% peak fraud rate. The surge created significant financial exposure and operational strain, making immediate, high-recall intervention a top priority for the organization’s survival and stability.

To bridge this gap, we engineered a high-performance LightGBM model across 2M+ accounts to predict and intercept fraudulent patterns. Recognizing that small teams have limited bandwidth, we didn't just aim for generic accuracy; we prioritized investigative efficiency. By setting an F1-optimal threshold of 0.8925, we ensured that the investigators' time was focused strictly on the highest-probability threats. This methodological choice allowed the system to maintain a low 1.08% False Positive Rate, limiting the manual review volume to just 1.35% of all transactions. This precision meant that roughly 1 in 5 flagged cases was true fraud, keeping the workload manageable for the risk team while still catching the bad actors.

The final model achieved an ROC-AUC score of 0.89, which effectively serves as an "A" grade in predictive certainty. In plain English, the system demonstrated an 89% success rate at correctly distinguishing a fraudulent actor from a legitimate customer. This level of precision allowed the bank to capture 25.1% of all fraud earlier in the funnel, protecting the bottom line without creating unnecessary friction for honest users. To ensure long-term trust and compliance, we utilized SHAP values to identify key drivers of fraud—such as Windows OS usage and housing status—ensuring the model remained explainable and transparent for leadership.

Beyond the technical deployment, we established a Digital Risk Committee (DRC) to unify leadership across Marketing, Risk, and CX. This transformation provided clear risk ownership and a proactive framework for sustainable, protected growth, moving the company from a reactive stance to a "logic-first" defensive posture.