Artificial Intelligence using Machine Learning techniques like Churn and Recommender models can help Relationship Managers connect with dormant clients and help recommend stocks and MFs using existing applications via different devices

1. Machine Learning for Stock Broking Houses

2. Page 2 © AlgoAnalytics All rights reserved
Overview
AlgoAnalytics: Company Profile
One Stop AI Shop
Solutions for BFSI Segment
Indicative Case Studies

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. Page 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 use text data analytics for
designing solutions like
sentiment analysis, news
summarization and many 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
BFSI
•Dormancy Analysis
•Recommender System
•Credit/Collection Score
Retail
•Churn Analysis
•Recommender System
•Image Analytics
Healthcare
•Medical Image Diagnostics
•Work flow optimization
•Cash flow forecasting
Legal
•Contracts Management
•Structured Document decomposition
•Document similarity in text analytics
Internet of Things
•Predictive in ovens
•Air leakage detection
•Engine/compressor fault detection
Others
•Algorithmic trading strategies
•Risk sensing – network theory
•Network failure model
© AlgoAnalytics All rights reserved

5. Page 5 © AlgoAnalytics All rights reserved
Predict Dormancy – Finding
which clients are unlikely to
transact and take action
Recommender System –
Suggesting products likely to
increase chance of action for a
particular customer, cross-up
sell
Credit score – Application,
behavior and collection scores,
estimation of default
Analytics and loans –
Origination, pricing and
valuation of loans
Channel adoption and
preference – Use demographics
and trading data to build a
classification model
Whole Gamut of Solutions
Automatic RFP Responses –
Developed Machine Learning
Based Question Answer System
to respond to RFPs.
Signature Verification – extract
the signature thru OCR and
validate the signature based on
type of the document .
Document Processing –
Contract decomposition,
document similarity and others
Automated News Download
and Summarization –
Automatic download of
relevant news items, News
summarization
Smart Inbox + Smart Reply –
Routing emails to appropriate
inbox and responding
automatically to client email
queries.
Virtual Relationship Manager/
Customer Support Assistant – Assistant
to increase accessibility for clients. This
was developed using Microsoft
framework and CNTK
Text Analytics, Image
Analytics,
Time Series Modelling,
Intent Analytics
BOT Apps
Text Analytics, Image
Analytics,
Client Analytics

6. Page 6 © AlgoAnalytics All rights reserved
Indicative Case Studies
Client Analytics

7. Page 7 © AlgoAnalytics All rights reserved
Client Analytics - Dormancy, Stock & Mutual Fund Recommendation
The relationship manager connects with the predicted dormant clients with the recommendation on
stocks and Mutual Funds real time on the existing Apps and devices.
Applications
Real-Time
Applications
Mobile Apps
Web Applications
Last 5 stocks
bought
Recommended Stock Probability
Intraday
INFOSYS_TECHNOLOG
IES
Intraday State Bank of
India
0.1629
Intraday
MOTHERSON_SUMI_
SYSTEM
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
Dormancy
Prediction
• RM wise list of predicted
dormant clients will be published
on the RM Dashboard.
• This input can be feed into the
stock and Mutual Fund
Recommendation systems
Stock
Recommender
• The system is designed to
recommend stocks for the
selected clients by RM or for the
entire list of clients
Mutual Fund
Recommender
• The system is designed to
recommend Mutual funds types
(Equity, Hybrid and Debt) for the
selected clients by RM or for the
entire list of clients
Recommended MF Probability
Equity MF 0.467
Hybrid MF 0.237
Debt MF 0.164
Integration with existing
Applications on various
devices

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. Page 9 © 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

10. Page 10 © AlgoAnalytics All rights reserved
Cross Selling Mutual Funds Approach
Inclination to
Invest in MF
•The inclination can be defined
differently as suitable for business.
•MF Buys/ (MF Buys + Equity TO)
•MF Buys/ (MF Buys + Intraday TO)
•Count of MF invested in.
•Even a custom objective required
by business.
•Median can be used as threshold to
label
Features
•Trading ratios {intraday, positional,
short term, mid term, long term,
open}.
•% of TO in NIFTY-50.
•Equity PnL,
•% of Equity TO in total TO.
•MF features like MF Buys. Value of
MF objective in the previous period
can be used as a feature.
Modeling
• Consecutive Period Setup: Example
• Train Features: Jul – Dec 15
• Train Labels: Jan – Jun 16
• Test Features: Jan - Jun 16
• Test Labels: Jul - Dec 16
• Same Period Setup: Example,
•Train Features, Label: Jul – Dec 15.
•Test Features, Labels: Jan – Jun 16
Client universe has MF Buys > 0 in label period and Equity TO > 0 in feature period.
Median is used as threshold to label. The same threshold used for train data is used as threshold for the test data.
The approach is applied independently to each type of MF such as Debt, Equity & Hybrid using the same set of
features.
Problem Statement: Cross selling mutual fund given the equity portfolio and buy sells for
clients

11. Page 11 © AlgoAnalytics All rights reserved
MF Prediction: Predicts high inclination customers to buy MF
MF Buys/ (MF Buys + Equity
TO) for Debt MF
MF Buys/ (MF Buys + Equity
TO) for Equity MF
MF Buys/ (MF Buys + Equity
TO) for Hybrid MF
10 – fold CV Out of Sample 10 – fold CV Out of Sample 10 – fold CV Out of Sample
Accuracy 70.23% 61.29% 74.43% 68.17% 70.59% 63.13%
Kappa 40.46% 25.20% 48.86% 37.00% 41.18% 26.83%
Sensitivity 75.77% 82.19% 75.30% 76.61% 78.55% 70.20%
Specificity 64.69% 44.14% 73.56% 62.90% 62.63% 56.95%
PPV 68.21% 54.68% 74.01% 56.28% 67.76% 58.77%
NPV 72.75% 75.14% 74.86% 81.18% 74.49% 68.62%
Prevalence 50.00% 45.06% 50.00% 38.40% 50.00% 46.64%
AUC 68.01% 76.90% 67.01%
Performances for Consecutive Period Model Setup

12. Page 12 © AlgoAnalytics All rights reserved
Further Areas Where ML can be implemented
First notice of loss in insurance
Claim processing
Fraud checking
Policy Renewal
Underwriting long term care and life
insurance applications
Matching records across various platforms while
onboarding customer
Monthly review of all accounts with zero balance, nil
activity in lockers etc and automatic emails to
accountholders
Credit card operations
Credit initiation: banks screening review
Wire transfers checking for beneficiary details and against
negative lists (AML/ frauds)
Portfolio management
Mortgage procession
Post trade operations – payment
record processing
Trading record keeping compliance
Monitoring trade performance
INSURANCE BANKING BROKING

13. Page 13 © AlgoAnalytics All rights reserved
Our Capabilities
 Achieve competitive advantage by
leveraging analytics to improve
decisions, enhance marketing, and
influence consumer behavior.
Predictive analytics to turbo-charge
decisions across lines of business for
marketing and risk management
Strong Predictive modeling
techniques using advanced
vizualization
Deep knowledge
of wholesale
banking & market
place; and
customer
behavior
Statistical skills
Advanced Expertise
in techniques like
time series, decision
trees clustering,
linear regression,
ensemble models
Advanced AI
techniques to
derive interesting
& unexpected
insights from data
for customer
retention
Help address the
right questions, find
the strategic
answers to leverage
your business

14. Interested in knowing more?
July 12, 2017
Contact: info@algoanalytics.com