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
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Overview
AlgoAnalytics: Company Profile
One Stop AI Shop
Solutions for BFSI Segment
Indicative Case Studies
3. Page 3 ยฉ 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
โข 20+ years in application of advanced mathematical techniques
to academic and enterprise problems.
โข Experience in application of machine learning to various
business problems.
โข Experience in financial markets trading; Indian as well as global
markets.
Highlights
โข Experience in cross-domain application of basic scientific
process.
โข Research in areas ranging from biology to financial markets to
military applications.
โข Close collaboration with premier educational institutes in India,
USA & Europe.
โข Active involvement in startup ecosystem in India.
Expertise
โข Vice President, Capital Metrics and Risk Solutions
โข Head of Analytics Competency Center, Persistent Systems
โข Scientist and Group Leader, Tata Consultancy Services
Prior Experience
โข Work at the intersection of mathematics and other
domains
โข Harness data to provide insight and solutions to our
clients
Analytics Consultancy
โข +30 data scientists with experience in mathematics
and engineering
โข 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
โข Develop advanced mathematical models or solutions
for a wide range of industries:
โข Financial services, Retail, economics, healthcare,
BFSI, telecom, โฆ
Expertise in Mathematics and Computer
Science
โข Work closely with domain experts โ either from the
clients side or our own โ to effectively model the
problem to be solved
Working with Domain Specialists
About AlgoAnalytics
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AlgoAnalytics - One Stop AI Shop
Aniruddha Pant
CEO and Founder of AlgoAnalytics
โขWe use structured data to
design our predictive analytics
solutions like churn,
recommender sys
โขWe use techniques like
clustering, Recurrent Neural
Networks,
Structured
Data
โขWe use text data analytics for
designing solutions like
sentiment analysis, news
summarization and many more
โขWe use techniques like natural
language processing, word2vec,
deep learning, TF-IDF
Text Data
โขImage data is used for predicting
existence of particular
pathology, image recognition
and many others
โขWe use techniques like deep
learning โ convolutional neural
network, artificial neural
networks and technologies like
TensorFlow
Image Data
โขWe use sound data to design
factory solutions like air leakage
detection, identification of
empty and loaded strokes from
press data, engine-compressor
fault detection
โขWe use techniques like deep
learning
Sound Data
BFSI
โขDormancy Analysis
โขRecommender System
โขCredit/Collection Score
Retail
โขChurn Analysis
โขRecommender System
โขImage Analytics
Healthcare
โขMedical Image Diagnostics
โขWork flow optimization
โขCash flow forecasting
Legal
โขContracts Management
โขStructured Document decomposition
โขDocument similarity in text analytics
Internet of Things
โขPredictive in ovens
โขAir leakage detection
โขEngine/compressor fault detection
Others
โขAlgorithmic trading strategies
โขRisk sensing โ network theory
โขNetwork failure model
ยฉ AlgoAnalytics All rights reserved
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Predict Dormancy โ Finding
which clients are unlikely to
transact and take action
Recommender System โ
Suggesting products likely to
increase chance of action for a
particular customer, cross-up
sell
Credit score โ Application,
behavior and collection scores,
estimation of default
Analytics and loans โ
Origination, pricing and
valuation of loans
Channel adoption and
preference โ Use demographics
and trading data to build a
classification model
Whole Gamut of Solutions
Automatic RFP Responses โ
Developed Machine Learning
Based Question Answer System
to respond to RFPs.
Signature Verification โ extract
the signature thru OCR and
validate the signature based on
type of the document .
Document Processing โ
Contract decomposition,
document similarity and others
Automated News Download
and Summarization โ
Automatic download of
relevant news items, News
summarization
Smart Inbox + Smart Reply โ
Routing emails to appropriate
inbox and responding
automatically to client email
queries.
Virtual Relationship Manager/
Customer Support Assistant โ 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
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Indicative Case Studies
Client Analytics
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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
โข RM wise list of predicted
dormant clients will be published
on the RM Dashboard.
โข This input can be feed into the
stock and Mutual Fund
Recommendation systems
Stock
Recommender
โข The system is designed to
recommend stocks for the
selected clients by RM or for the
entire list of clients
Mutual Fund
Recommender
โข 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
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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
โข Train data
โข 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 โ Sep 2015 Oct โ 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
โข INR 1.6 M brokerage from 2,200 (11% of 20K - CRM assigned )
reactivated clients.
โข INR 309 K brokerage from 1,881 (4.8% of 39K โ CRM not assigned )
reactivated clients
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Stock Based Recommender System
Data Filtering
โขDiscard Short Lived Sessions
โขRemove Rare Items
โขConsider only top โnโ most
popular items
Training and Testing
โขTraining, Validation and
Testing set
โขDeep learning
โขFinal Recommendations
Evaluation
โขRecall: Number of times
actual next item in the
sequence is in top โkโ
recommendations
Observed Recall@5 โ 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
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Cross Selling Mutual Funds Approach
Inclination to
Invest in MF
โขThe inclination can be defined
differently as suitable for business.
โขMF Buys/ (MF Buys + Equity TO)
โขMF Buys/ (MF Buys + Intraday TO)
โขCount of MF invested in.
โขEven a custom objective required
by business.
โขMedian can be used as threshold to
label
Features
โขTrading ratios {intraday, positional,
short term, mid term, long term,
open}.
โข% of TO in NIFTY-50.
โขEquity PnL,
โข% of Equity TO in total TO.
โขMF features like MF Buys. Value of
MF objective in the previous period
can be used as a feature.
Modeling
โข Consecutive Period Setup: Example
โข Train Features: Jul โ Dec 15
โข Train Labels: Jan โ Jun 16
โข Test Features: Jan - Jun 16
โข Test Labels: Jul - Dec 16
โข Same Period Setup: Example,
โขTrain Features, Label: Jul โ Dec 15.
โขTest Features, Labels: Jan โ 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
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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 โ fold CV Out of Sample 10 โ fold CV Out of Sample 10 โ 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
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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 โ payment
record processing
Trading record keeping compliance
Monitoring trade performance
INSURANCE BANKING BROKING
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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