Hi, my name is

Fauzan Risqullah.

Technical Consultant | Data Analytics – Risk & Anti-Fraud.

I'm a Technical Consultant based in Indonesia, delivering real-world projects across the banking & insurance sectors — with a focus on detection systems, risk management (asset–liability management), and regulatory reporting. I'm passionate about analytics, data science, & data engineering, and bring that data-driven mindset to every solution I build.

View my work Contact me 
class BriefIntro:
    def __init__(self):
        self.name     = "Fauzan Risqullah"
        self.role     = "Technical Consultant"
        self.domains  = ["Banking",
                         "Insurance",
                         "Fintech"]
        self.focus    = ["Fraud Detection",
                         "Risk Management(ALM)",
                         "Regulatory Reporting"]
        self.interest = ["Data Analytics",
                         "Data Science",
                         "Data Engineering"]
    def build(self, data):
        return insight + impact

01. About me

I'm a data and risk analyst with 3+ years delivering fraud detection, risk-control systems, and regulatory reporting for some of Indonesia's largest banks (KBMI 4 and KBMI 3) and top local insurers. I specialize in behavioral analysis, user/entity profiling, anomaly detection, and rule design on large-scale transaction data.

I work hands-on across the full lifecycle of Fraud Detection Systems — IBM Safer Payments and SAS Risk Stratum — from feature engineering and behavioral profiling through scenario design, data integration, and detection tuning.

I'm strong in Python, SQL, and BI visualization (SAS Visual Analytics, Tableau, Power BI), with anti-fraud and transaction-monitoring experience that transfers directly to abuse detection, risk segmentation, and data-driven decision-making in fast-paced fintech environments.

Education

Hacktiv8 Bootcamp

Sep 2023 – Dec 2023

Full-Time Data Science Program

Jakarta, Indonesia

BINUS University

2019 – 2023

B.Sc. in Information Systems · GPA 3.78 / 4.00

Tangerang, Indonesia

  • Specialization: Enterprise Resource Planning (ERP)
  • Thesis: Requirements Analysis of an Anti-Money Laundering (AML) System Using a Graph Database

Technical skills

Risk & Anti-Fraud

  • IBM Safer Payments
  • SAS Risk Stratum
  • Behavioral Profiling
  • User/Entity Profiling & Risk Tagging
  • Anomaly Detection
  • Velocity & Out-of-Pattern Analysis
  • Transaction Monitoring
  • Fraud Rule Design
  • AML Screening

Data Analysis

  • User Behavior Analysis
  • Customer/User Segmentation
  • Feature Engineering
  • A/B Testing & Experimentation
  • Exploratory Data Analysis
  • Data Mining
  • User Journey Mapping
  • Descriptive & Inferential Statistics

Programming & Databases

  • Python
  • SQL
  • SAS
  • PostgreSQL
  • SQL Server
  • Google BigQuery
  • TigerGraph · Neo4j
  • MongoDB
  • Git / GitHub

Machine & Deep Learning

  • Scikit-Learn
  • Classification & Regression
  • K-Means / Clustering
  • PCA
  • Recommender Systems
  • Hyperparameter Tuning
  • TensorFlow / Keras
  • LSTM / NLP
  • Computer Vision
  • PMML Integration

Data Engineering

  • ETL
  • Data Mapping & Integration
  • Data Warehouse
  • Apache Airflow
  • Amazon RDS
  • Great Expectations
  • Docker
  • Apache Spark · AWS Glue
  • Linux (Ubuntu · CentOS · RHEL)

Visualization & BI

  • SAS Visual Analytics
  • Tableau
  • Power BI
  • Looker Studio
  • Kibana
  • Matplotlib / Seaborn

02. Professional experience

Risk, anti-fraud, and data engineering work across Indonesia's banking and insurance sectors.

Technical Consultant

PT. IDX Consulting · Jakarta, Indonesia

Jan 2024 — Present

Risk & anti-fraud consulting for KBMI 4 & KBMI 3 banks and leading insurers — delivering Fraud Detection Systems, regulatory reporting, and ALM assessments end to end.

  • Delivered an end-to-end FDS implementation (IBM Safer Payments) for a KBMI 4 state-owned bank: system configuration, fraud-rule design, and user-level behavioral profiling on large-scale transaction data.
  • Engineered behavioral profiles and risk tags (counter/formula logic) to flag out-of-pattern behavior, velocity abuse, and transaction anomalies in near real-time, with data mapping between the FDS and the bank's Data Warehouse.
  • Designed test scripts and ran A/B testing on detection rules and models, then led Vendor Internal Testing (VIT) and supported SIT/UAT through to production sign-off.
  • Built a live AI/ML FDS proof-of-concept (competitive tender) for a regional development bank (BPD): 19 detection rules and 21 behavioral-profiling components across authentication, out-of-pattern, velocity, and card-fraud scenarios.
  • Implemented IFRS17 regulatory reporting for top insurers and acted as Business Analyst on ALM assessments (OJK gap analysis; IRRBB, LCR, NSFR) — cutting ETL runtime from ~2 hours to ~30 minutes.
IBM Safer PaymentsFraud DetectionSAS Risk StratumPythonSQLA/B TestingIFRS17ALM

Technical Consultant Intern

PT. Indonesia Global Solusindo (ISGS) · Jakarta, Indonesia

Feb 2022 — Feb 2023

Data integration & visualization on graph-database and data-virtualization platforms.

  • Built an automated data pipeline from a graph database to a visualization tool (with a staging DB) and shipped dashboards for three use cases within two months.
  • Created unified data views by combining RDBMS, NoSQL, and semi-structured sources using a data-virtualization tool.
Graph DatabaseNoSQL DatabaseRelational Database (RDBMS)Data VirtualizationETLDashboards

Web Designer & Data Entry Intern

PT. Global Dinamika Teknologi (GDT) · Jakarta, Indonesia

Feb 2018 — Apr 2018

Early hands-on role in website customization and data quality.

  • Customized website features — adding new functionality and adjusting existing features to client demand.
  • Entered and validated data in the client's portal, resolving incomplete information and data discrepancies.
WebData EntryData QA

03. Professional projects

Selected client engagements in fraud detection, financial risk, and regulatory reporting across Indonesian banking & insurance. Client names are withheld for confidentiality.

End-to-end implementation

Internal Fraud Detection System — Branch Channel

KBMI 4 state-owned bank · IBM Safer Payments

2024 — 2025

Delivered an end-to-end internal fraud detection system for the branch channel (deposit segment) — expanding monitoring of financial and non-financial transactions performed at branches and automating an Early Warning System to branch managers.

  • Configured IBM Safer Payments end to end — mandator segregation, risk lists, detection rules, and behavioral-profiling components on large-scale branch transaction data.
  • Engineered user-level behavioral profiles and risk tags (counter/formula logic) to flag out-of-pattern behavior, velocity abuse, and transaction anomalies in near real-time.
  • Built the data mapping and integration between the FDS and the bank's data warehouse (batch CSV feeds from ODS via ETL), ensuring detection accuracy across channels.
  • Automated email Early Warning Information (EWI) to branch managers and configured the case-investigation workflow and case states for the anti-fraud team.
  • Designed test scripts and ran A/B testing on rules and models; led Vendor Internal Testing (VIT) and supported SIT/UAT through to production sign-off.
IBM Safer PaymentsBehavioral ProfilingFraud RulesETL / Data MappingA/B TestingSIT/UAT
Competitive tender · Proof of Concept

AI/ML Fraud Detection System

Regional development bank (BPD) · IBM Safer Payments

2024 — 2025

Designed and demonstrated the AI/ML fraud-detection solution, technical proposal, and proof-of-concept for a limited competitive tender — proposing IBM Safer Payments for real-time payment fraud and mapping the bank's risk requirements to model-factory, behavioral-analytics, and anomaly-detection capabilities.

  • Designed detection across four scenario groups: authentication anomalies, behavioral out-of-pattern, high-risk velocity transactions, and debit-card / ATM / EDC fraud.
  • Built a live proof-of-concept with 19 detection rules and 21 behavioral-profiling components — including pre-seeded counters (rolling 30-day averages, 90-day maximums) as features for user-level risk profiling and anomaly scoring.
  • Authored the technical proposal and compliance matrix, mapping client requirements to IBM Safer Payments' AI/ML capabilities along with the implementation approach, scope, and delivery model.
IBM Safer PaymentsAI/MLAnomaly DetectionBehavioral ProfilingProof of ConceptTechnical Proposal
End-to-end implementation

IFRS17 / PSAK 74 Regulatory Reporting

Two leading local insurers · SAS Risk Stratum

2024

Implemented the SAS Solution for IFRS17 to bring two leading local insurers into PSAK 74 / IFRS17 compliance ahead of the OJK deadline — an end-to-end flow from data preparation and ETL through CSM calculation to regulatory disclosure reporting.

  • Built the end-to-end IFRS17 process: SAS data integration from the insurer's data warehouse, Insurance Contract Grouping, discounting, expense allocation, Risk Adjustment, and CSM calculation across GMM, PAA, and VFA measurement models.
  • Configured the sub-ledger accounting module, process manager (workflow & governance), and reporting engine to produce regulatory disclosure reports.
  • Owned requirement gathering, configuration, and ETL/data integration; performed SIT/UAT and pipeline troubleshooting across ~8-month engagements.
  • Re-engineered the processing script to cut ETL runtime from ~2 hours to ~30 minutes.
SAS Risk StratumIFRS17 / PSAK 74ETLCSMGMM · PAA · VFASIT/UAT
Competitive tender · System assessment

ALM — IRRBB, Balance Sheet & Liquidity Risk

KBMI 3 & 4 banks · Business Analyst

2025 — 2026

Supported competitive tenders and system assessments for asset-liability management — proposing the Regnology OneSumX (Risk Hub) platform to cover IRRBB, balance-sheet, and liquidity risk, with outputs aligned to OJK regulatory reporting.

  • Performed gap analysis of ALM / risk systems against OJK requirements and defined the target IRRBB, LCR, and NSFR reporting outputs.
  • Specified IRRBB measurement across Economic Value of Equity (EVE) and Net Interest Income perspectives with parallel and non-parallel rate-shock scenarios, plus Funds Transfer Pricing (FTP) and behavioral (prepayment) modeling.
  • Contributed to the technical proposals for the IRRBB and balance-sheet / liquidity-risk system procurement, where the proposed solution validated 20+ ALM test scenarios with full calculation transparency.
Regnology OneSumXIRRBB (EVE / NII)LCR · NSFRFTPBehavioral ModelingGap Analysis

04. Other projects

End-to-end projects built during the Hacktiv8 Full-Time Data Science program — from analytics and ML to deep learning and data engineering.

Final Project

SmartSelectPro — Recommender & Customer Segmentation

Team of 3 · Role: Data Engineer

End-to-end fashion e-commerce system combining a product recommender (content-based + collaborative filtering via cosine similarity) with K-Means customer segmentation. I built the automated Airflow pipeline feeding cleaned data into Amazon RDS & BigQuery, powering a Looker dashboard. Deployed with Streamlit.

  • Airflow
  • PostgreSQL
  • BigQuery
  • Amazon RDS
  • K-Means
  • Looker
  • Streamlit
Looker dashboard →
Phase 0 · Milestone

London Crime Analysis & Dashboard

Role: Data Analyst

Analyzed Greater London Authority crime data to locate hotspots and dominant offence types. Combined descriptive & inferential statistics with an interactive Tableau dashboard — surfacing Westminster as the top hotspot and theft/violence as leading categories, with a focused-patrol recommendation.

  • Python
  • Pandas
  • SQL
  • Statistics
  • Tableau
Phase 1 · Milestone

Telco Customer Churn Prediction

Supervised classification · deployed

Built a churn-prediction model for a telecom provider. Benchmarked KNN, SVM, Decision Tree, Random Forest and Boosting with cross-validation and hyperparameter tuning inside a scikit-learn pipeline — optimizing for Recall to minimize missed churners. Shipped as an interactive web app.

  • Scikit-Learn
  • Pipeline
  • Cross-Validation
  • Streamlit
  • Hugging Face
Phase 2 · Milestone

Credit Default Profiling Pipeline

Automated ETL · data validation

Designed an automated pipeline for a bank's Risk Management team to profile credit customers. Orchestrated with Airflow: extract from PostgreSQL → clean → validate with Great Expectations → load to Elasticsearch → visualize in Kibana, scheduled to run daily.

  • Apache Airflow
  • PostgreSQL
  • Great Expectations
  • Elasticsearch
  • Kibana
  • Docker
Deep Learning

Financial News Sentiment Analysis

NLP · LSTM

Trained an LSTM neural network to classify financial-news sentiment into negative, neutral, or positive. Leveraged LSTM's long-term memory to capture full-sentence context, with a dedicated inference notebook for unseen headlines.

  • TensorFlow / Keras
  • LSTM
  • NLP
  • Python
Machine Learning

Credit Card Customer Segmentation

Unsupervised · K-Means

Segmented credit-card customers from six months of usage behavior using K-Means. Selected the optimal cluster count via the silhouette score and validated the model on unseen data to confirm stable, interpretable segments.

  • Scikit-Learn
  • K-Means
  • Silhouette Score
  • Clustering
Machine Learning

Credit Card Default Prediction

Supervised classification

Predicted credit-card payment default from customers' transaction & payment history plus demographics. Evaluated with F1-score and Recall to minimize false negatives — the costly error in a financial-risk setting.

  • Scikit-Learn
  • Classification
  • Feature Engineering
  • Python

05. Publications & recognition

Selected publications, contributions, and speaking — largely around graph databases and data science.

Graph Database Applications and Concepts — book cover
Book · Author

Graph Database Applications and Concepts

Elex Media Komputindo · Published June 2024

An authored book introducing graph-database concepts and their real-world applications.

  • Book · Co-Author

    Revolution in Data Science, AI & Cybersecurity in a Connected World — Impacting Geopolitics, Social, Economy & Trade

    Asosiasi Big Data dan AI (ABDI) · November 2023

  • Journal Paper

    Product Recommendation System Using Graph Database

    Journal of Theoretical and Applied Information Technology · Vol. 101 No. 12 · June 2023

  • Speaker · Certificate

    Webinar: “Pengenalan Graph Database”

    Indonesia Artificial Intelligence Society (IAIS) · September 2023

06.

Let's build something with data.

I'm currently open to data analyst, data science and data engineering opportunities.

Say hello
GitHub LinkedIn Email