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Home› Courses› Financial Analytics Course
💰 High-Salary Specialisation 📍 Dilsukhnagar, Hyderabad ✅ Certificate on Completion

Financial Analytics Course

Apply data science and AI to the most data-rich industry on earth — finance. From risk modelling and fraud detection to algorithmic trading analysis and investment analytics. Taught by an active Financial Data Scientist with 7+ years in BFSI companies building models used by real financial institutions.

⏱
DURATION
8 Weeks
🎯
LEVEL
Beginner to Advanced
💻
MODE
Online · Offline · Hybrid
🏆
CERTIFICATE
Industry Certificate

Tools & Technologies You Will Learn

Python Pandas NumPy Scikit-learn XGBoost SQL Power BI Excel Advanced Tableau Statsmodels Prophet Bloomberg API concepts
Enroll on WhatsApp → View Curriculum
Financial Analytics Course at LearnAI Tech Hub 💰
Free Demo Available
Contact us for current batch fees & EMI options
  • ⏱ Duration: 8 Weeks
  • 🎯 Level: Beginner to Advanced
  • 💻 Online · Offline · Hybrid
  • 🏆 Certificate on completion
  • 💼 100% placement assistance
  • 📁 Real project + internship
  • 🔁 Lifetime access to recordings
  • 📞 Mentor support throughout
Enroll Now on WhatsApp Book Free Demo Class Call: +91 93900 23585

Free career counselling available daily · No pressure, just honest advice

About This Course

Financial Analytics combines the highest-demand skill with the highest-paying industry

Banks, insurance companies, investment firms and FinTech startups are among the heaviest users of data science and AI globally. Credit risk models decide who gets loans. Fraud detection models protect billions in transactions every day. Algorithmic systems influence market movements. The financial sector both generates enormous amounts of data and has the regulatory and commercial pressure to use it intelligently — making it one of the most consistent employers of analytics and AI talent.

At LearnAI Tech Hub, this course is built specifically around financial analytics use cases — not generic data science applied loosely to finance. You work with real financial datasets, build credit scoring models, construct fraud detection pipelines, perform time series forecasting on market data and create financial dashboards that communicate risk and performance to stakeholders. By week 8, you have a portfolio of financial AI projects that positions you for roles in BFSI, FinTech, insurance and financial consulting.

What makes this course different

🏭
Taught by an active BFSI Data Scientist building live risk models

Your trainer has built fraud detection models that process millions of transactions daily. Every technique comes from real financial institution experience.

💳
Fraud detection, credit scoring and risk — the core BFSI AI use cases

The three ML applications that every bank, insurance company and FinTech is actively deploying. You build all three during this course.

📊
Finance-specific statistics and modelling techniques

Time series forecasting with financial data, portfolio risk analysis, credit risk modelling — techniques specific to finance that generic data science courses do not cover.

🌏
Opens doors across BFSI, FinTech, insurance, investment and consulting

Financial analytics is in demand across the broadest range of employers — from large banks to FinTech startups to Big 4 consulting firms globally.

Course Curriculum

7 modules · 8 Weeks · Real projects every week

Every module is taught by an active MNC professional using real tools, real datasets and real architectures — not textbook examples.

01
Financial Data Ecosystem & Python for Finance
8 topics · Week 1
Financial markets: equity, fixed income, derivatives, forex — data types and sources
BFSI data: credit data, transaction data, market data, insurance claims
Python libraries for finance: Pandas, NumPy, Matplotlib, yFinance
Financial data cleaning: missing values, outliers, look-ahead bias
Regulatory context: Basel III, RBI guidelines, SEBI regulations — what they mean for data
Project: Exploratory financial data analysis — banking customer dataset
02
Credit Risk Modelling
10 topics · Weeks 2–3
Credit risk fundamentals: PD, LGD, EAD — the three components of credit risk
Credit scoring: logistic regression, scorecard development
Feature engineering for credit: bureau variables, behavioural variables
Model validation: Gini coefficient, KS statistic, PSI, CSI for credit models
Application scoring vs behavioural scoring — key differences
IFRS 9 and Expected Credit Loss (ECL) calculations
Reject inference for building models from biased sample data
Project: End-to-end credit scorecard for personal loan approval
03
Fraud Detection & Anomaly Detection
10 topics · Week 4
Fraud patterns: card fraud, identity theft, money laundering, insurance fraud
Class imbalance handling: SMOTE, cost-sensitive learning, threshold tuning
Fraud detection algorithms: Random Forest, XGBoost, Isolation Forest
Network analysis for fraud: graph-based fraud ring detection concepts
Real-time fraud scoring vs batch fraud detection — architecture differences
Rule engines combined with ML models — hybrid fraud systems
Model monitoring: detecting concept drift in fraud models
Project: Credit card transaction fraud detection system with XGBoost
04
Financial Time Series Analysis
10 topics · Week 5
Financial time series properties: stationarity, autocorrelation, seasonality
Stock price analysis: returns, volatility, rolling statistics
ARIMA and SARIMA for financial forecasting
Prophet for business financial forecasting — revenue, sales, demand
LSTM for financial time series — deep learning approach
Feature engineering for time series: lag features, technical indicators
Backtesting frameworks: evaluating predictive models on historical data
Project: Revenue forecasting model for a BFSI organisation
05
Investment Analytics & Portfolio Analysis
8 topics · Week 6
Portfolio theory: return, risk, diversification, Sharpe ratio, Sortino ratio
Modern Portfolio Theory: mean-variance optimisation
Factor models: CAPM, Fama-French three-factor model
Risk metrics: VaR (Value at Risk), CVaR, stress testing
Algorithmic trading concepts: signal generation, backtesting, execution
Sentiment analysis for financial markets using NLP and news data
ESG analytics: Environmental, Social and Governance data analysis
Project: Portfolio risk and return analysis dashboard
06
Insurance Analytics
6 topics · Week 7
Insurance data: premiums, claims, actuarial tables, policy data
Claim frequency and severity modelling: GLMs for insurance
Churn prediction for insurance policyholders
Customer lifetime value (CLV) in insurance context
Telematics data analysis for usage-based insurance
Catastrophe modelling concepts: natural disaster risk quantification
Project: Insurance claims frequency prediction model
07
Financial Dashboards & Career Preparation
8 topics · Week 8
Power BI for financial reporting: P&L, balance sheet, cash flow dashboards
Financial KPI dashboards: NPA ratio, ROA, ROE, RAROC, cost-to-income
Executive MIS reports and regulatory reporting concepts
FinTech analytics tools: introduction to Bloomberg, Refinitiv concepts
Financial analytics interview preparation: technical and case study rounds
Capstone: Complete financial analytics portfolio project presentation
HR placement referral and mock BFSI interviews
Get Full Curriculum on WhatsApp →
Tools & Technologies

You will graduate using industry tools — not toy projects

Every tool in this course is currently used by professionals in live production environments across companies worldwide.

🐍
Python
📊
Power BI
⚙️
Scikit-learn
📊
XGBoost
🗄️
SQL
📑
Excel
📈
Tableau
📉
Statsmodels
🔮
Prophet
🐼
Pandas
🧮
NumPy
💹
FinTech APIs
Career Outcomes

Roles you will qualify for after this course

Our placement team directly places students from this course into these roles across our 1,000+ client company network — startups to Fortune MNCs.

💰
Financial Data Scientist
₹7L – ₹20L / year
Banks, NBFCs, investment firms, insurance companies, BFSI MNCs across India, UAE, UK
🔍
Risk Analyst (AI/ML Focus)
₹6L – ₹16L / year
Credit risk, market risk, operational risk teams in banks, insurance, consulting firms
💳
Fraud Analytics Specialist
₹7L – ₹18L / year
Banks, card networks, payment gateways, FinTech companies, fraud investigations firms
📈
FinTech Data Analyst
₹5L – ₹14L / year
FinTech startups, digital lending companies, payment platforms, crypto analytics
🔬
Quantitative Analyst
₹10L – ₹30L / year
Investment banks, hedge funds, algorithmic trading firms, asset management companies
💼
Financial Analytics Consultant
₹8L – ₹22L / year
Big 4 consulting, risk advisory firms, BFSI-focused technology consulting practices
Average Starting Salary — Freshers (Hyderabad)
Based on our placed students · 2024–2025 batch data
₹5L – ₹20L per year
Who Is This For

This course is designed for you if…

📊
Data professionals wanting the highest-paying specialisation

BFSI is among the highest-paying industries for data scientists. Financial analytics expertise commands a consistent premium over generic data science salaries.

💼
Finance professionals wanting to add AI and data skills

Chartered accountants, MBAs in finance and banking professionals who add Python and ML skills become Financial Data Scientists — a transformation that typically doubles salary.

🧑‍💻
Data scientists wanting domain differentiation

Generic data science is competitive. Financial analytics is specialised, in demand and commands a significant salary premium. This course provides domain differentiation that generic portfolios lack.

🎓
MBA Finance and B.Com graduates wanting tech-adjacent careers

Finance graduates who understand data science bridge a gap that is enormously in demand — especially in risk, fraud, investment and FinTech analytics roles.

🏢
Banking and insurance professionals targeting analytics roles

If you work in a bank or insurance company and want to move from operations into the analytics or data science team, this course provides the exact technical skills that internal team transfers require.

🌏
Professionals targeting UAE, UK or USA finance roles

Financial analytics is globally transferable. UK, UAE and USA financial institutions actively hire Indian analytics professionals with BFSI domain knowledge and ML skills.

Prerequisites

Basic mathematics (algebra, statistics concepts) Any graduation — B.Com, BBA, B.Tech, MBA — all welcome No prior programming or data science experience required Interest in finance and data
FAQs

Questions about Financial Analytics Course?

Not finding your answer? WhatsApp us directly — we respond within 30 minutes.

Ask on WhatsApp →
No. Week 1 covers all the financial domain context you need. However, candidates with a finance background (CA, MBA Finance, banking professionals) will find that their domain knowledge accelerates learning and makes the projects significantly more meaningful.
Yes. Python is taught from scratch in the first week. The course is designed for finance professionals who have zero programming experience — we build from first principles to complete financial ML models.
Banks (private, public, foreign), NBFCs, insurance companies, FinTech startups, payment gateways, investment firms, stock broking companies, asset management companies, Big 4 consulting, risk advisory firms and any corporate treasury or FP&A function.
Financial modelling in Excel is a business planning skill — projecting P&L, DCF valuations, scenario analysis. Financial Analytics is a data science skill — building predictive models, fraud detection systems, risk scoring and time series forecasting using Python and ML. Both are valuable; this course teaches the AI/ML side.
Yes. UAE banks and FinTech companies actively recruit Indian financial analytics professionals. UK financial institutions (especially in risk and fraud) have significant demand. Our abroad consulting team and HR team can guide you on specific pathways.
Yes — Week 6 covers algorithmic trading concepts, signal generation, backtesting and risk metrics (VaR, Sharpe). However, for a full-depth algorithmic trading course, the quantitative finance space requires additional specialisation beyond this course.
Ready to start?

Enroll in Financial Analytics Course today

Book your free demo class — meet your trainer, see the teaching style, ask everything you want. No commitment, no fees.

Enroll on WhatsApp → Call: +91 93900 23585

📍 Dilsukhnagar, Hyderabad · Online across India & Internationally · Mon–Sat 9AM–8PM

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