Professional Certificate in Financial Analytics Course
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"The size of the Financial Analytics industry in India is 2 billion USD in revenue while the global Financial Analytics market is expected to grow to 11.5 billion USD by 2023." - (Source). The Financial Analytics market size is around $ 1.5 billion in terms of annual revenue. From algorithmic high-frequency trading to payment wallets or P2P lending every aspect of the financial services market is infused with analytics technology. Financial Analytics Course enables evidence-based rather than intuition-based decision making. With the help of advanced analytics, businesses can identify future markets and avenues for greenfield ventures. Statisticians can develop strategic pricing models to boost revenue growth. The rumble in fintech has led to a surge in the requirement for Financial Analytics Course professionals in our country. Most financial companies are moving up the value chain with the help of savvy analytics professionals.
Overview of Financial Analytics Course
This Financial Analytics Course program explores the myriad possibilities of improved financial transaction performance and revenue growth that ensue when machine learning approaches are integrated with traditional humdrum financial processes. This course attempts to uncover the potential applications of Data Science and Machine Learning in the BFSI industry. This financial analytics course is specifically designed to enable learners to get the required set of skills to advance their careers in the finance industry. Analytics enables automation and advanced statistical software to analyze gigantic volumes of data. In the Insurance sector, financial analytics course makes it possible to create individualized tailor-made products to meet the future needs of customers, Data Analytics gives insurers the freedom to assess the risk and profitability accurately. This translates to better underwriting decisions and generates premium calculations adjusted for risk. Data Analytics can also be used to prioritize claims. This enables faster settlement of straightforward claims.
In the banking industry financial analytics course helps us in analyzing the historical transactions and trading of customers and deliver better-targeted services which will mean customer retention. We can also assess the risk profile and creditworthiness of customers well in advance. Early warning loan default models can predict a loan default well in advance and reduce the risk cost. This enables better fraud detection. Advanced analytics allows banks to provide a faster response to routine requests e.g. the position of an ATM, the amount of cash to be stacked in one. Advanced analytics can be used to tap real-time data from smartphones and the internet from mobile banking and internet banking customers. This data then can be examined to improvise upon the customer experience. Machine Learning enables banks to develop new business models and identify a better source of income from data collected from customers.
Learning Outcomes of Financial Analytics Course
Machine Learning and Big Data Analytics have become a game-changer in the financial analytics domain. The Certification Program in Financial Analytics Course is a sui generis attempt to blend machine learning solutions for traditional finance problems. Specifically designed to suit the financial service industry (FSI) professionals and data professionals who wish to understand the application of Big Data Analytics, Machine Learning, Neural Networks, and Deep Learning to financial industry data. Employing Machine Learning solutions has transformed many back-office operations in financial firms from cost to profit centers and empowering ‘self-driving’ finance. According to this Bloomberg article, through Natural Language Processing techniques JPMC software reviewed legal documents in seconds what lawyers took 360,000 hours to do! This is one of the many incredible examples of Machine Learning and Artificial Intelligence in the Finance Industry. This financial analytics course is meant for professionals from the finance domain as it provides a comprehensive picture of how data science and artificial intelligence can be leveraged to increase the speed and accuracy of financial transactions. Understanding the applications of Data Science, Machine Learning to FSI will be the prime objective of this content-rich program. Having a deep appreciation for Financial Analytics is imperative for the success of various Banks, Insurance Firms and Financial Institutions at large.
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Who Should Sign Up?
- Financial Analysts
- CFO/Director/Head of Finance
- Business Analysts
- Data Analysts
- Risk Managers
- Certified Financial Analysts
- Credit Analysts
- Financial Advisors
- Math, Science and Commerce Graduates
Modules of Financial Analytics Certification Course
The introductory module of this course contains a description of basic terminologies in BFSI. The core applications of Data Analytics in Stock Market Investments, Banking, Financial Advisory, and Electronic Payment Services are discussed. The various applications of Artificial Intelligence in Finance namely Algorithmic Stock Trading, Automated Robo Advisors, Fraud Detection, and Prevention are also explored. For the uninitiated, to operations research, this course comes with a module on the fundamentals of Data Optimization and Data Simulation. Students will solve Linear Programming problems using Simplex techniques, duality and sensitivity analysis.
No course in financial analytics is complete without a discourse on key topics of financial management and this course is no exception. Understand the theory behind investments,risk-return tradeoff, decision making under uncertainty, indifference curves, and quadratic utility functions. Topics like Capital Allocation Line, Optimal Portfolio, Optimal Allocation, analysis using indifference curves find mention here. A separate module is devoted to key concepts in portfolio math and investment in portfolios. The features of the Capital Asset Pricing Model and Monte Carlo Pricing model are discussed. Students learn to apply the Black- Scholes formula. The concluding modules deal with the application of Black-Box Techniques, Neural Networks, and Deep Learning in scanning financial documents. Participants will also develop an AI-driven chatbot.
Learn about Financial Data Analytics with respect to Data Science in FSI, Machine Learning in FSI, AI in FSI, etc. Key areas in finance where AI is applicable would be discussed, namely about, Algorithmic Stock Trading, Automated Robo-Advisors, Fraud Detection and Prevention.
Learn about the preliminaries of Data Optimization & Data Simulation which serves as a good reference for people to understand the core Financial Analytics concepts. Learn on solving Linear Programming problems using Simplex techniques.
Understand about investments, how to trade consumption across time, trade-off between risk and return, decision making under uncertainty, indifference curves, quadratic utility function, etc.
Understand about Capital Allocation Line (CAL) and Optimal Portfolio. Learn about Lending and Borrowing on the CAL, highest utility allocation, feasible set of investment opportunities, optimal allocation, analysis using indifference curves, etc.
Understand about portfolio math, diversification in action by taking the example of two risky assets, mean-standard deviation diagram, mean-variance frontier, efficient frontier, two funds separation, constraints on investment, diversification in well-diversified portfolios, tangency, MVE, etc.
Features of Markowitz analysis, expected returns from historical averages, efficient frontier, resampling based on simulation, estimation error, means and covariances, asset pricing models, market portfolio, pricing model, security market line, equilibrium, disequilibrium, full equilibrium, BU & TU price determination, Beta value etc.
- Detect Credit Card Fraud using Decision Tree Algorithm.
- Time Series Forecasting using SARIMAX.
- Time series Forecasting using Facebook’s Prophet Library.
- Predict financial crisis of countries in the continent using Multi-layered Perceptron.
- Replicating the index using Autoencoders to streamline passive investment strategies in the market.
- Building Robotic Trading algorithms using Reinforcement Learning.
Learn about various regulatory bodies such as Basel III, SEC, SROs, GLBAct, etc. Also learn about the GDPR Act, safeguarding PII and many more regulatory norms laid down for various countries. Understand about Connected Banks, Fly-in Financial Markets, Financial Asset Exchange, Open Banking and Data Sharing.
Trends in Financial Analytics Course
Top financial institutions look for new-age skills in their analysts like Data Analytics, Marketing Analytics, Predictive Modelling, and Business Intelligence. The use of predictive modeling for stock market movement, detecting financial statement fraud, personal loan default prediction, credit score prediction, credit card fraud detection are a few areas where financial analytics are elaborately used. Currently, 70,000 professionals are employed in the Financial Analytics sector in India. Ernst and young alone have an 800 member analytics practice with budding data scientists on board.
The demand for Financial Analytics Course professionals is fuelled by top companies and by startups from Tier1 and Tier2 cities. Mumbai is the financial hub with 33% of Analytics professionals working in the sprawling metropolis. Bangalore boasts the second-highest concentration of Analytics professionals - around 24%. The growth in the BFSI sector is due to the implementation of emerging technologies with Advanced Analytics, Machine Learning, and Robotic Process Automation becoming the key pillars of growth. The BFSI sector is investing big time in Data Analytics to improve organizational success, enhance return, transform business operations, cut risks, and offer flawless service.
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