Business & Data Analyst — London, UK
Finding the signalin the noise.
I'm Ashutosh Parab — an MSc Business Analytics graduate who turns messy data into decisions, from crypto fraud detection at 96.2% ROC-AUC to macroeconomic forecasting.
About — Click a concept
The page demonstrates what I do.
I hold an MSc in Business Analytics and Decision Sciences from the University of Leeds, built on a Computer Science engineering foundation. My work spans fraud detection, forecasting, and customer analytics — RFM segmentation, CLV modelling, behavioural analysis — always with the goal of an insight someone can act on.
Rather than list my skills, this section shows them. Each concept below re-arranges the same data points into the idea being named — because analysis is about making structure visible.
Unlabelled points resolve into behavioural clusters — the basis of targeted strategy.
Selected Work
Projects
Dissertation research on wallet-level fraud in peer-to-peer cryptocurrency transactions using the Elliptic++ dataset. LightGBM classifier with SHAP interpretability achieved 96.2% ROC-AUC across 39 validated fraud indicators, and proved fraud is instantaneous (98.3% single time-step) rather than evolving over time.
96.2%
ROC-AUC
39
Fraud indicators
98.3%
Single time-step
Machine learning pipeline predicting Initial Coin Offering success. Compared Random Forest, XGBoost, and Logistic Regression with engineered features and hyperparameter tuning, evaluated on ROC-AUC to identify the strongest signals of fundraising success.
3
Models compared
ROC-AUC
Evaluation
ARIMA and Prophet forecasting models for monthly US Personal Consumption Expenditure, decomposing trend and seasonality and surfacing macroeconomic insight through interactive Power BI dashboards.
Monthly
Horizon
ARIMA + Prophet
Models
K-Means and RFM-based clustering of e-commerce customers into behavioural segments, enabling targeted marketing strategy with clearly differentiated value, frequency, and recency profiles.
K-Means + RFM
Method
Dissertation — University of Leeds · 2025 · Supervised by Dr. Liz Mason
Illicit funds don't hide in big moves.They hide in small, fast ones.
My MSc dissertation investigated wallet-level fraud in P2P cryptocurrency transactions on the Elliptic++ dataset — engineering 39 behavioural features across 1.27M records and applying SHAP explainability to 28,601 flagged wallets. A LightGBM classifier with time-aware train/test splits reached 96.2% ROC-AUC.
The key finding: high-frequency, low-value transactions are the primary mechanism for masking illicit fund flows, with transaction cadence and counterparty diversity the strongest predictive signals. A temporal ARIMA overlay turned this into early-warning fraud signals 2–4 weeks ahead at ~0.80 confidence — directly applicable to AML compliance and transaction monitoring in fintech and RegTech.
96.2%
ROC-AUC
1.27M
Records
2–4 wks
Early warning
Top fraud indicators · mean |SHAP| (illustrative)
Transaction cadence
Counterparty diversity
Mean transaction value
Txn frequency bursts
Wallet age at first txn
In/out flow ratio
Experience & Education
Business Analyst (Freelance)
Chandgad Farm Fresh — India
Segmented a 200+ customer base into 3 behavioural groups through RFM clustering and CLV modelling, shaping a differentiated marketing strategy that contributed to ~15% revenue growth and a diversification plan across 3 new product lines.
2025
MSc Business Analytics & Decision Sciences
University of Leeds — Leeds, UK
Advanced Forecasting, Machine Learning, Data Visualisation, Quantitative Analysis, Evidence-Based Consultancy, Advanced Decision Making. Dissertation: fraud detection in P2P cryptocurrency transactions (96.2% ROC-AUC).
2024 — 2025
BEng Computer Science
Visvesvaraiah Technological University — India
Foundations in programming, algorithms, and databases that underpin my analytics engineering.
2019 — 2023
Contact
Let's find yoursignal together.
Actively seeking Business Analyst and Data Analyst roles in the UK — particularly in fintech, RegTech, and anywhere data shapes decisions. Graduate Route Visa holder — no sponsorship required, available immediately.