A detailed advisor-focused case study covering portfolio benchmarking, risk–return analysis, client clustering, debt–return detection, visual behaviour modelling, and actionable insights for better wealth management decisions.

Table of Contents

• Introduction — The Advisor’s Reality
• What This Case Study Explores
• Why Benchmarking Matters in Modern Wealth Management
• How We Approached the Analysis
• Step 1 — Benchmarking Portfolio Returns
• Step 2 — Exploring Correlations Between Risk and Return
• Step 3 — Visualising Portfolio Behaviour
• Step 4 — Clustering Client Archetypes
• Step 5 — Detecting High-Debt, High-Return Clients
• Step 6 — Analysing Risk-Adjusted Efficiency
• One-Page Advisory Report (Prompt)
• Client Archetype Explanations (Prompt)
• Top Return-to-Risk Asset Allocation Insights (Prompt)
• How Advisors Can Apply These Insights
• Closing Thoughts

1. Introduction — The Advisor’s Reality

Financial advisors today sit at the intersection of data overload and decision urgency. Every client portfolio generates dozens of data points—risk scores, holdings, returns, transactions, behavioural trends. Yet the advisor’s most valuable hours are not spent crunching spreadsheets; they are spent interpreting what the data means for client goals.

The challenge: turning large, messy financial data into clear, risk-adjusted insights quickly enough to act.That question guided this case study: Can Terno.ai act as a no-code financial analyst—one that benchmarks thousands of portfolios, surfaces patterns, and highlights actionable insights, all through natural-language prompts?

2. What This Case Study Explores

The entire chat history with Terno.AI can be seen here.
We used a dataset of 10,000 hypothetical wealth-management clients(Bank_Customers.csv)  from Kaggle with variables including:

• Demographics: Age, Gender, Country
• Financial metrics: Net Assets, Debt, Credit Score
• Portfolio data: Portfolio Return, Risk Profile, Diversification
• Product holdings: Real Estate, Bonds, ETFs, Insurance

The goal was to simulate how an advisor could upload data once, ask questions in plain English, and get dashboards, CSVs, and visuals without writing a single line of code.

3. Why Benchmarking Matters in Modern Wealth Management

Benchmarking gives context to performance.
It answers questions like:

• Are a client’s returns appropriate for their risk?
• Do investors in one country outperform others?
• Which segments are quietly over- or under-leveraged?
  

Traditionally, this required complex spreadsheets or coding. Terno.ai compresses the entire workflow into conversational analytics — giving every advisor access to quant-grade insights.

4. How We Approached the Analysis

We followed a progressive, no-code workflow inside Terno.ai:

1. Upload dataset (uncleaned) to test automatic profiling.
2. Ask benchmark questions via prompts.
3. Iteratively explore correlations, clusters, and outliers.
4. Export CSVs and visuals for presentation or follow-up.
   

This mirrors how advisors think — ask, observe, and refine — while the platform handles the heavy computation.

5. Step 1 — Benchmarking Portfolio Returns
Purpose

The first step was to establish baselines and segment performance by Country, Age, and Risk Profile — giving advisors a reference point for client comparisons.

Prompt used

“Using Bank_Customers.csv, compute the global mean and median of Portfolio Return.
Then compute summary statistics (count, mean, std, min, median, max, delta vs global mean) grouped by Country, Age, and Risk Profile.”

Insights

• Global benchmark: Mean ≈ 0.1299, Median ≈ 0.1302.
• By Country: France showed the highest average return (~0.1301), slightly above the global mean; Germany the lowest (~0.1293).
• By Age: Younger (18–25) and older (80 +) cohorts displayed greater variance, while middle-aged groups clustered tightly near the benchmark.
• By Risk Profile: Moderate risk levels (≈ 0.02–0.03) delivered the strongest average returns; extremely low or high risk trailed slightly.

Key Takeaways

These segmented benchmarks let advisors shift from absolute to relative performance:

“Your portfolio returned 13.2%, around 0.2% above peers with similar risk in France.”

Why It Matters

Terno.ai replaces hours of manual filtering with a single prompt. The resulting summaries serve as the analytical base for client reviews and compliance reports.

6. Step 2 — Exploring Correlations Between Risk and Return

Purpose

After benchmarking, we tested whether risk, diversification, and wealth indicators truly explain return differences.

Prompt used

“Compute Pearson correlations between Risk Profile, Portfolio Return, Diversification, Net Assets, Debt, and CreditScore.
Generate an interactive heatmap and save it as correlation_heatmap.html.
Provide a short interpretation (3–4 bullets) focused on advisor implications.”

Terno AI Output

Caption:Terno.ai automatically computed pairwise correlations and generated an interactive Pearson correlation heatmap (correlation_heatmap.html).

Insights

• Risk Profile ↔ Portfolio Return: moderate positive (~+0.30)
• Diversification ↔ Return: slight negative (~–0.10)
• Net Assets ↔ Return: weak positive (~+0.15)
• Debt ↔ Credit Score: moderate negative (~–0.40)
  

Interpretation

These findings quantify long-held intuitions:

• Higher risk brings potential reward — but not linearly.
• Diversification curbs volatility while slightly capping upside.
• Wealthier clients access better products, boosting performance.
• Debt discipline directly improves credit health and stability.
  

Why It Matters

Advisors can now replace general advice (“more risk could raise returns”) with evidence-based recommendations, improving both trust and documentation.

7. Step 3 — Visualising Portfolio Behaviour

Purpose

Visualisation transforms statistical outputs into intuitive stories that advisors can communicate easily to clients or team members.

Prompts used

• “Plot Portfolio Return vs Age by Country (scatter + smoothed lines).”
• “Create boxplots of Portfolio Return by Country.”
• “Create a hexbin scatter of Risk Profile vs Portfolio Return and highlight mean return.”

Terno AI Outputs

a. Portfolio Return vs Age by Country (Scatter + LOWESS)

Caption:
Smoothed scatter showing portfolio-return trends across France, Germany, and Spain. Each line represents a LOWESS-fitted trajectory of return by age.

b. Boxplot of Portfolio Return by Country

Caption:
Boxplots illustrate median returns (≈ 0.13) and outlier ranges.
Terno.ai noted: “France shows the highest medians, while Germany displays greater return variability.”

c. Risk Profile vs Portfolio Return (Hexbin Density)

Caption:
Hexbin plot displaying distribution density of returns by risk level. The dashed red line indicates global mean return (0.13).

Insights

• Country trends: France leads slightly; Germany has wider variability.
• Age patterns: Younger investors display higher dispersion.
• Risk-return density: Higher risk increases upside spread but with inconsistency.
• Outlier concentration: Germany’s tail spread implies sporadic outperformance or underperformance — valuable for local advisor diagnostics.
  

Key Takeaways

• Visuals enable conversations such as:
  • “You sit right on the French median line for your age group.”
  • “Your volatility mirrors higher-risk investors.”
• Terno.ai transforms data into dialogue — turning charts into advisory tools.

8. Step 4 — Clustering Client Archetypes

Purpose

While benchmarking and correlation analysis reveal what drives performance, clustering helps uncover who your clients are in behavioural and financial terms.
The goal was to automatically segment thousands of clients into distinct archetypes based on their Net Assets, Portfolio Return, and Debt — enabling more targeted advisory strategies.

Prompt used

“Cluster clients using Net Assets, Portfolio Return, and Debt (standardise variables first).
Try k = 4 clusters as default..”

Terno AI Output

Caption:
Terno.ai identified four distinct client clusters based on their standardised Net Assets, Portfolio Returns, and Debt.

Cluster Profiles:

Terno.ai Insight:
Cluster 1 clients show the healthiest debt-to-asset ratio, while Cluster 3 combines higher assets and higher leverage — a watchlist for risk monitoring.

Key Takeaways

Each cluster represents a unique advisory archetype:

• Cluster 1: Capital preservation, ideal for conservative wealth plans.
  
• Cluster 2: Growth-focused investors seeking higher yield.
  
• Cluster 3: High-asset clients using leverage—require monitoring.
  
• Cluster 0: Stable, balanced investors suitable for scaled automation.
  

Why It Matters

Clustering creates immediate operational value — segment-specific strategies, targeted engagement, and smarter CRM segmentation — in seconds.

9. Step 5 — Detecting High-Debt, High-Return Clients

Purpose

Identify outliers with Debt above the 90th percentile and Portfolio Return above the global mean — potential high-leverage outperformers.

Prompt used

“Identify clients whose Debt is above the 90th percentile and whose Portfolio Return is above the global mean.”

Terno AI Output

 Terno.ai instantly filtered outliers and exported their records to outliers_high_debt_high_return.csv for deeper review.

Insights 

• Count: 502 clients (~5%)
• Debt threshold: ≈ $39.5K
• Return threshold: > 0.1299
• Segment profile: High-leverage, high-return investors — strategic but risk-sensitive.
  

Advisor Actions

• Review leverage ratios and stress-test portfolios.
• Flag clients exceeding sustainable debt levels.
• Balance performance chasing with long-term stability.
  

Why It Matters

Terno.ai converted a complex filtering task into one query — a ready-to-act list for advisory follow-up and compliance oversight.

10. Step 6 — Analysing Risk-Adjusted Efficiency

Purpose

After identifying high-debt outperformers, the next question was efficiency:
Which clients generate the best returns per unit of risk?

In wealth management, this is known as the Return-to-Risk Ratio — a measure of portfolio efficiency that balances performance and volatility.
For advisors, understanding which clients maximise this ratio provides actionable insight into which investment strategies yield superior results with less risk exposure.

Prompt used

“Create a new column Return_to_Risk = Portfolio Return ÷ Risk Profile for all clients where Risk Profile > 0.
Identify the top 5% of clients by Return_to_Risk, save as top_return_risk_ratio_clients.csv, and report count.
Also provide the top 10 CustomerIDs with their ratio and suggest three reasons why these clients may have high ratios.”

Terno AI Output

Caption:
Terno.ai computed the Return-to-Risk metric for all eligible clients, identified the top 5% performers (≈ 492 clients), and saved the file top_return_risk_ratio_clients.csv for deeper analysis.

Insights

• Total qualified clients: 492 (top 5% by Return-to-Risk).
• Top 10 clients’ ratios: 17.23 to 17.76 — indicating exceptional efficiency.
• Data visualisation: A scatter plot (return_to_risk_scatter.png) highlights the top 5% in red, showing how they outperform peers even at moderate risk levels.
  

Reasons Behind High Ratios (advisor interpretation)

Terno.ai suggested three plausible explanations for these outliers:

1. Superior diversification or strategy execution — portfolios that achieve high returns with minimal volatility or measured risk.
   
2. Alternative or tactical positions — clients using hedging, leverage, or timing strategies to capture gains efficiently.
   
3. Understated formal risk scores — some clients may appear unusually efficient if their recorded “Risk Profile” understates true market exposure.
   

Key Takeaways

For advisors, these clients exemplify “efficiency champions” — investors who get the most out of every unit of risk they take.
Studying this cohort helps firms uncover best practices in portfolio construction, identify potential model portfolios, and flag any anomalies in risk assessment.

Practical use cases:

• Benchmark efficient portfolios as models for similar client types.
• Compare advisory approaches across teams or branches.
• Use as early indicators for potential risk underestimation or misclassification.
  

Why It Matters

Traditional metrics like raw return or volatility often mislead.
The Return-to-Risk ratio standardises performance evaluation — making it an invaluable internal KPI for both advisors and portfolio managers.

By generating this analysis instantly, Terno.ai provides:

• Data-driven recognition for high-performing clients or teams.
• Quality control over risk assessments.
• Actionable efficiency maps for internal portfolio optimisation.

This closes the feedback loop between risk assessment and actual performance outcomes — something that typically takes analysts hours of spreadsheet modelling.

Prompt: One-Page Advisory Report

Prompt used
“Using the outputs summary_by_country.csv, summary_by_age.csv, summary_by_risk_profile.csv, correlation_heatmap.html, clients_with_clusters.csv, outliers_high_debt_high_return.csv, and top_return_risk_ratio_clients.csv, generate a one-page advisory report (300–400 words). Include sections titled Findings, Recommendations, and Client Talking Points.”

Prompt: Client Archetype Explanations

Prompt used
“For each of the three core client archetypes identified through clustering (Moderate Balance–Low Return, Affluent–Low Leverage, Mid-Wealth–High Yield), write a 40–60 word plain-English explanation that an advisor can use in a client meeting. Include one recommended action per archetype.”

Prompt: Top Return-to-Risk Asset Allocation Insights

Prompt used
“Using top_return_risk_ratio_clients.csv, compute the average allocation across each asset class (e.g., ETF Tech, RealEstate, Bonds, PrivateEquity). Compare these averages to the overall population and highlight which asset classes are over-represented in the top return-to-risk group. Export the comparison as a CSV.”

11. How Advisors Can Apply These Insights

1. Start with the clients who need attention right now

The return-to-risk rankings and high-debt outliers highlight who needs a quick review.
Efficient performers are great for strategy check-ins, while high-leverage clients may need a risk discussion.

2. Use the clusters to simplify your client conversations

Each segment tells a straightforward story:moderate-balance clients need a small performance boost,affluent low-debt clients suit income ideas,and high-yield clients may need some volatility control.It helps frame advice in a way clients instantly understand.

3. Back up recommendations with simple data points

The correlations make it easy to explain why changes might help:
a little more risk can increase returns,
diversification trades a bit of upside for stability,
and high debt tends to drag down credit health.
These are quick, confidence-building talking points.

4. Borrow ideas from the top performers

The most efficient portfolios lean slightly more into ETF Tech and ETF Health.
For the right clients, a measured tilt in these areas can sharpen long-term efficiency.

5. Drop the one-page report straight into meeting prep

The automated summary gives you findings, recommendations, and talking points in one place.
It saves time and standardises how advice is delivered across clients.

6. Use tags in your CRM to stay organised

Label clusters and outlier groups so follow-ups become automatic, not manual.
Segments like “Efficiency Leader” or “High-Leverage Watchlist” make next steps clear.

Closing Thoughts

Terno.ai transforms the advisor’s workflow from reactive to proactive.
By turning everyday data into digestible insights, it enables advisors to focus on relationships and strategy instead of spreadsheets.

This case study illustrates how a single upload and a series of conversational prompts can reproduce the analytical depth of an entire quant desk—without writing a single line of code.

Ready to experience data-driven Advisory insights?
Explore the full analysis  @ https://rohan0.app.terno.ai/chat/share/87d28405-e869-4fba-8a8b-1789343711f9

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