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Data Science for Retail Broking


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This Presentation presents how Data science can bring manifold benefits to Retail Broking. Machine Learning & Text Analytics can impact your business in many positive ways- gives you that competitive edge and gains you customer satisfaction & loyalty

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Data Science for Retail Broking

  1. 1. Data Science for Retail Broking
  2. 2. Page 2 © AlgoAnalytics All rights reserved Overview About AlgoAnalytics Applications of Analytics in Retail Broking Retail Broking Case Studies
  3. 3. Page 3 © AlgoAnalytics All rights reserved CEO and Company Profile Aniruddha Pant CEO and Founder of AlgoAnalytics PhD, Control systems, University of California at Berkeley, USA 2001 • 20+ years in application of advanced mathematical techniques to academic and enterprise problems. • Experience in application of machine learning to various business problems. • Experience in financial markets trading; Indian as well as global markets. Highlights • Experience in cross-domain application of basic scientific process. • Research in areas ranging from biology to financial markets to military applications. • Close collaboration with premier educational institutes in India, USA & Europe. • Active involvement in startup ecosystem in India. Expertise • Vice President, Capital Metrics and Risk Solutions • Head of Analytics Competency Center, Persistent Systems • Scientist and Group Leader, Tata Consultancy Services Prior Experience • Work at the intersection of mathematics and other domains • Harness data to provide insight and solutions to our clients Analytics Consultancy • +30 data scientists with experience in mathematics and engineering • Team strengths include ability to deal with structured/ unstructured data, classical ML as well as deep learning using cutting edge methodologies Led by Aniruddha Pant • Develop advanced mathematical models or solutions for a wide range of industries: • Financial services, Retail, economics, healthcare, BFSI, telecom, … Expertise in Mathematics and Computer Science • Work closely with domain experts – either from the clients side or our own – to effectively model the problem to be solved Working with Domain Specialists About AlgoAnalytics
  4. 4. AlgoAnalytics - One Stop AI Shop Aniruddha Pant CEO and Founder of AlgoAnalytics•We use structured data to design our predictive analytics solutions like churn, recommender sys •We use techniques like clustering, Recurrent Neural Networks Structured Data •We used text data analytics for designing solutions like sentiment analysis, news summarization and much more •We use techniques like natural language processing, word2vec, deep learning, TF-IDF Text Data •Image data is used for predicting existence of particular pathology, image recognition and many others •We use techniques like deep learning – convolutional neural network, artificial neural networks and technologies like TensorFlow Image Data •We use sound data to design factory solutions like air leakage detection, identification of empty and loaded strokes from press data, engine-compressor fault detection •We use techniques like deep learning Sound Data Retail •Churn Analysis •Recommender System •Image Analytics – image recognition, tagging, original and substitute differentiation Healthcare •Medical Image Diagnostics •Work flow optimization •Cash flow forecasting Legal •Contracts Management •Structured Document decomposition •Document similarity in text analytics Internet of Things •Assisted Living •Predictive in ovens •Air leakage detection •Engine/compressor fault detection Others •Algorithmic trading strategies •Risk sensing – network theory •Network failure model •Multilanguage sentiment analytics
  5. 5. Page 5 © AlgoAnalytics All rights reserved Technology:
  6. 6. Page 6 © AlgoAnalytics All rights reserved Data Science for Broking Predict Dormancy – Finding which clients are unlikely to trade and take action Stock Recommender System – Suggesting stocks that will increase probability of trading for a particular customer Dormancy visualization Identifying KPIs for visualizing Dormancy RM change – Assessing impact of change of relationship manager on trading activity Mobile Brokerage – Origination, pricing and valuation of loans Dynamic pricing models - Predicting brokerage slabs and sensitivities of trading volumes to broking charges Channel adoption and preference – Use demographics and trading data to build a classification model Portfolio Analytics – Analysis of client portfolio and suggestions for changes as per risk profile Risk profiling – margin and default risks Document similarity in text analytics – Automatic email classification, determining the topic of complaint etc. Automated news download – downloading news that is relevant for customers Sentiment analysis – Using text analytics to decide customer response to various offerings News Summarization – Automatic download of relevant news items, News summarization
  7. 7. Page 7 © AlgoAnalytics All rights reserved Customer churn  Customer churn prediction : take customers past activity data and use distributed file systems and cluster computing to predict churn Process Data Compute Features Train the classification model Predict / Score • Target with loyalty programs Predicted Returning Customers • Target with other offers and discounts Predicted ‘Not Returning’ Customers
  8. 8. Page 8 © AlgoAnalytics All rights reserved Q5 Customers with no trades were marked as DORMANT Test data Label Q1 Q2 Q3 Q4 Q2 Q3 Q4 Q5 Modelling (Machine learning Algorithms) Result Evaluation Prediction • Train data • Data aggregation quarter-wise Trades data Roughly 6% of the clients responsible for ~80% of the loss Past Brokerage Number of Trades Margin Amount Exchange Ledger Amount Examples of features used Client profiles in terms of attributes computed from past trading. - Active clients = 1.03Mn - Active clients for which trade data is available = 346K - Average count of active clients who traded at least once during train period = 254K Prediction for quarter Jul – Sep 2015 Oct – Dec 2015 Accuracy 81.10% 78.30% Sensitivity 88.35% 75.42% Specificity 72.78% 81.90% Prevalence 53.4% 55.57% AUC 89.56% 88.21% Total clients 252845 255873 % Growth in Nifty -5.01% -0.03% Dormancy Prediction: predicts customers likely to stop trading • INR 1.6 M brokerage from 2,200 (11% of 20K - CRM assigned ) reactivated clients. • INR 309 K brokerage from 1,881 (4.8% of 39K – CRM not assigned ) reactivated clients
  9. 9. Page 9 © AlgoAnalytics All rights reserved Recommender System What is RecSys? Value of Recommendation RecSys Modeling and Applications  Aims to predict user preferences based on historical activity and implicit / explicit feedback  Helps in presenting the most relevant information (e.g. list of products / services) Collaborative filtering: User’s behavior, similar users Content-based filtering: using discrete characteristic of items *Movies, music, news, books, search queries, social tags, etc. * Financial services, insurance Intel business unites (BUs), sales and marketing  Nearest Neighbor modeling  Matrix factorization and factorization machines  Classification learning model
  10. 10. Page 10 © AlgoAnalytics All rights reserved Stock Based Recommender System Data Filtering •Discard Short Lived Sessions •Remove Rare Items •Consider only top ‘n’ most popular items Training and Testing •Training, Validation and Testing set •Deep learning •Final Recommendations Evaluation •Recall: Number of times actual next item in the sequence is in top ‘k’ recommendations Observed Recall@5 – 30.39% Last 5 stocks bought Recommended Stock Probability Intraday INFOSYS_TECHNOLOGIES Intraday State Bank of India 0.1629 Intraday MOTHERSON_SUMI_SYST EM Intraday L&T 0.137 Intraday UTI BANK LTD Longterm ICICI Banking Copora 0.124 Intraday ASIAN PAINT Intraday ICICI Banking Copora 0.0709 Intraday LIC HOUSING FIN Intraday Bharat Forge 0.061
  11. 11. Page 11 © AlgoAnalytics All rights reserved RM Change Risk Evaluation RM’s Customer Ranking •Rank clients under an RM in terms of their net worth and brokerage Change in Ranking •Determine changes in ranking of net worth and brokerage Develop Classification Model •Use RM and client features to determine whether a client will rank worse after an RM change • Evaluating the impact of the RM change by identifying the customers at risk who are associated with/managed by the same RM leaving the organization : Take 90 days before and after an RM change Sum pre and post brokerage and net worth Sort clients according to brokerage and net worth ranking Develop model to predict change in ranking on RM change • Results based on changes in net worth and brokerage revenue: Accuracy Sensitivity Specificity Prevalence Kappa AUC Brokerage 74.53% 70.92% 77.02% 40.88% 0.4764 83.09% Net Worth 74.88% 73.53% 76.07% 46.76% 0.4957 82.28% • Results:
  12. 12. Page 12 © AlgoAnalytics All rights reserved Cluster Discovery, Description & Visualization using Predicted Labels Cluster Discovery  Cluster analysis can help identify groups or clusters of similar clients.  Functional description of each cluster can help make inferences on client dormancy.  Cluster analysis can be performed for predicted dormants.  Features such as traded days, net worth, turnover (TO) may group clients into separate clusters. Cluster visualization Prominent 2D View of Clusters 3D Visualization for Cluster Separation Cluster Size Q4 Traded Days Q4 Min Net Worth (Lacs) M3 Traded Days M3 Peak Net Worth (Lacs) Q4 NSE Traded Days 13736 1.48 1.43 0.00 0.00 1.27 373 17.97 0.20 0.76 0.19 17.68 7952 1.22 0.60 1.07 0.82 1.09 Cluster Description Each cluster can be used as a business action item for reaching out to clients  Represent each cluster by a single point in the feature space.  Make inferences on client behavior from these representation.
  13. 13. Page 13 © AlgoAnalytics All rights reserved Mobile Brokerage Analysis  For example: In June there are 8693 clients on mobile, 5390 clients remained till Jan 2016. We computed different brokerage features based on the client present in June. Take the number of mobile and non-mobile users in June 2016 Trace the same set clients in the following months Analyze the difference in their trade behavior over the months
  14. 14. Page 14 © AlgoAnalytics All rights reserved Non-Mobile For Clients with Mobile Brokerage<=0 in June' 15 (# of Traded Clients on Mobile Total # of Traded Clients Total Brokerage Average Total brokerage Drop in average total brokerage in non-mobile Ratio of total clients retained in non-mobile customers June 2015 0 48669 121861024.8 2503.873612 1 1 July 2015 1878 32408 128207743 3956.052303 1.57997284 0.6659 August 2015 3625 31692 124188548.5 3918.608751 1.56501859 0.6512 September 2015 3475 29102 95712270.28 3288.855415 1.313506959 0.5980 October 2015 2968 24533 94536048.35 3853.423892 1.538984984 0.5041 November 2015 2970 23876 84523884.11 3540.11912 1.413856954 0.4906 December 2015 3575 24383 75354303.71 3090.444314 1.234265299 0.5010 January 2016 3179 23220 63526022.15 2735.832134 1.092639868 0.4771 Mobile For Clients with Mobile Brokerage>0 in June' 15 # of Traded Clients on Mobile Total # of Traded Clients Total Brokerage Average Total brokerage Drop in average total brokerage in mobile Ratio of total clients retained in mobile customers June 2015 8693 8693 31603303.92 3635.488775 1 1 July 2015 5412 7244 31868831.43 4399.341721 1.21011011 0.8333 August 2015 5474 7089 31138534.24 4392.514351 1.208232131 0.8155 September 2015 4606 6417 23532889.71 3667.272824 1.008742717 0.7382 October 2015 4036 5693 20816096.02 3656.437031 1.005762157 0.6549 November 2015 3874 5565 19891240.14 3574.346835 0.98318192 0.6402 December 2015 3961 5556 17288433.75 3111.669141 0.855914936 0.6391 January 2016 3823 5390 14874570.71 2759.660613 0.759089296 0.6200 Mobile Brokerage Analysis: Results 0 0.2 0.4 0.6 0.8 1 1.2 Jun-15 Jul-15 Aug-15 Sep-15 Oct-15 Nov-15 Dec-15 Jan-16 Comparison of clients retained Ratio of total clients retained in mobile customers Ratio of total clients retained in non-mobile customers0 0.5 1 1.5 2 Jun-15 Jul-15 Aug-15 Sep-15 Oct-15 Nov-15 Dec-15 Jan-16 Comparing average drop in total brokerage in mobile and non-mobile Drop in average total brokerage in mobile Drop in average total brokerage in non-mobile Total mobile clients retained = 0.62 Total non-mobile clients retained =0.48 More mobile clients are retained than the non-mobile clients.
  15. 15. Page 15 © AlgoAnalytics All rights reserved Text Analytics for Retail broking
  16. 16. Page 16 © AlgoAnalytics All rights reserved Text analytics Identifying Important News Item News Summarization Automatic summarization is the process of reducing a text document with computer program in order to create summary that retains the most important points of the original document Creation of algorithm that separates out market/ stock relevant news and filters out noise using machine learning  Supervised and unsupervised machine learning approaches can be tried to enhance the performance of the model.  Latest research from Natural Language Processing(NLP) and text analytics can yield interesting results. Extraction •Includes topic detection, scoring and extraction of most relevant text segment ML Techniques •Techniques like RNNLM (recurrent neural network language modeling and recurrent convolutional neural networks will be applied
  17. 17. Page 17 © AlgoAnalytics All rights reserved Document Similarity in Text Analytics Problem Statement - Finding semantically similar clause from standard clause library for each clause from input document
  18. 18. Page 18 © AlgoAnalytics All rights reserved Algorithmic Trading Strategy Methodology Technology Infrastructure
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