JOIN INDIA’S 1st BANK-LED DATATHON
The data analytics capabilities at YES BANK enables us to serve our customers and clients with greater depth, sophistication and efficiencies through innovations such as artificial intelligence, machine learning, natural language processing and bots. But, we understand that not all innovation can come from within the bank.
YES BANK has launched DATATHON, which invites participants from across the world, who will join us in our quest of Data driven Innovation and develop models for the appropriate sourcing and usage of data across businesses.
YES BANK has launched DATATHON, which invites participants from across the world, who will join us in our quest of Data driven Innovation and develop models for the appropriate sourcing and usage of data across businesses.
Top Solutions
Oracle
The team has worked on a ‘master product’ which creates a single 360 degree view of every retail customer , and also provides customized product and service recommendations for every individual customers – (including product propensity/predictive service delivery/service resolution etc
To create the ML recommendation engine they have used individual and cluster wise product propensity and a cluster correlation technique. The solution will allow the bank to provide customized and predictive products and services to each and every customer, as well as allow sales managers to cluster their clients based on the propensity model
Insights
The team sas developed a unique model basis , using a modified version of Shapley’s value – called Shap Value to create a customer – product/service propensity model basis product holdings/demographics/transactions. For eg. the propensity of a customer with 2 FD holdings towards an MF investment. This model combined with Team Oracle creates a comprehensive Next Best Action model
Finance Data Dons
Product 1 : The team has created a data model which creates a personal finance management tool for every customer using transaction patterns as a base. The application will have the ability to predict and classify transactions as well as smart investment advisory once implemented
Product 2: The model will help optimizing Merchant relationships – both online and offline retailers. The team has used ML to cluster POS transactions and identify anomalies in transaction volume and volume. This will help to increase POS/gateways, identify new merchants, provide merchant offers
Django Unchained
The team has created an AI based application for relationship managers (sales representatives) of the bank which will enable them to measure share of wallet for the bank for every retail customer, predict attriction as well as provide customized products/services
Billa
This team has created a master algorithm which can form the basis/foundation of other data models. Basically, this model which takes a cue from representation learning helps to tag, classify and then sort data in a much quicker and innovative way. Almost 70% of the time spent by any data scientist on creating a working prototype goes in cleaning, tagging and classifying data. This model will help accelerate that process and remove manual involvement in the same.
Reverse Atlas
The team has worked on a unique ML algorithm (which finds usage on streaming sites) which helps identify customer relationships basis transactions and creating relationship trees for every customer basis transactions. This would help identify and target new customers with exact products/solutions/offers as well as deepen current relationships
This almost creates a social media like network using Financial transactions and demographics as the base. This is the first time this algorithm which finds usage in streaming sites will find an application in the financial services space.
NLP Rockers (TCS)
Uses NLP and clustering methods to convert email service requests into service tags across the entire customer bases and subsequent clustering and prioritization. The model then uses predictive analytics to create customer clusters and predict service requests per cluster
Zessta
This product works on lowering customer attrition using a clustering algorithm which identifies customers most prone to attrition.This product works on lowering customer attrition using a clustering algorithm which identifies customers most prone to attriction also measures the affinity/non-affinity of customers to products to help predict next best action for customers for both dormant and active customers
Avensis
Chat bots have been quicker and better but a missing link has been customization. This solution will help YES BOT provide more customized responses to customers/non customers using demographic and account data, while also providing regular services like payment reminders/EMI tracker and relevant investment notification
Meson Labs
This product takes a unique approach to managing finance and investment using neural networks to create human like learning as well as use Google adwords to help classify transactions since transaction markers are often commonly known brands or words. This helps create a relevant map of all transactions for individual customers, comparison of spends and investments and accordingly provide advisory”
Prayaas
The team has worked on 2 solutions, the first one helps manage and predict individual customer deliveries like cards/statements/tax statements/cheque books – providing proactive service, reducing human involvement and as a result reducing service requests
Data pirates
The model provides an alternative method to score and profile customers , pooling in LinkedIn APIs and other external APIs to help score customers who are currently not scoped by CIBIL
GSA07
A major problem that bank customers face is to connect to the bank to avail a service, address any queries or resolve an issue. To address this concern this model will put in place which anticipates a customer’s concern based on his demographics and transaction data and allows the bank to reach to their customers with a solution before the customer knocks at their door with a problem.
Fakedata
The model uses machine learning to create a customer satisfaction or ‘happiness index’ , and basis affinity of customers to products or brands (basis transactions) – provides customized offers/offering as well as enables more nuanced and targeted marketing
Data Ninjas
Team has worked on use cases for properly classifying merchants based on there business category(Wallet, E.Commerce, Online Shopping, etc.) and then using the result to get to know the affinity of a customer segment with a particular merchant.
For Merchant classification, they used 2 approaches, unsupervised clustering approach to cluster merchants on correct spelled centroids and second approach to hit Google search API and doing POS tagging to cluster based on Nouns and adjectives. For checking user affinity they used RFM model.
Winning Teams
1. Data Wizards – Team Oracle
2. Data Acers :
a. Team Billa
b. Team Insight
c. Team Finance Data Dons
d. Reverse Atlas & Django Unchained
3. Data Pros
a. Team Seg Fault
b. Team WLP Rockers
c. Team Data Ninja & Zessta
d. Team Data Pirates & GSA 07
4. Data Champs – Team Insight
WHY PARTICIPATE?
Face the real-world Challenges
Access curated datasets (anonymized) and design your data models efficiently
Network with the community and have fun
Connect with like-minded data enthusiasts: explore and grow as experts together.
Mentorship
Acces to mentorship by industry experts in the field of Data Science, Big Data, FinTech and other emerging technologies.
Chance to work with YES BANK
Top 10 teams to get a working project with YES BANK to implement their data models
Win Cash Prizes
- Data Wizard – INR 5 Lakhs (1 Winner)
- Data Acers – INR 2 Lakhs Each(4 Teams)
- Data Pros – INR 1 Lakh Each(4 Teams)
- Data Champs – INR 1 Lakh (1 Student Team)
Unlock Access and Credits
All top 50 teams to get free AWS credits, access to Datagiri meetups and the opportunity to work with Cloudera tools
WHO CAN PARTICIPATE?
We’re inviting data scientists, analysts, AI/ML engineers, developers, startups, students, and anyone who is interested in working on Curated Banking Data* to join us for the journey to transform the ‘Future of Banking’ by building data-driven solutions.
We’re inviting data scientists, analysts, AI/ML engineers, developers, startups, students, and anyone who is interested in working on Curated Banking Data* to join us for the journey to transform the ‘Future of Banking’ by building data-driven solutions.
HOW TO PARTICIPATE?
Step 1: Click here ‘Join the Datathon’ – you will be led to our datathon partner – Skillenza’s page for the YES BANK Datathon
Step 2: Click ‘Register’ and enter details required to sign up
Step 3: Get your crew together and form a team – we recommend cross-disciplinary teams (max. 4 members) comprising Data Scientist, Big Data Engineer, Coder, Full Stack Developer to register
Step 4: Once completed, you will get a confirmation mail from Skillenza. Happy data diving!
Datasets & Use Cases
Top 50 teams get access to curated banking data*
- Service Tickets & Leads
- Customer Product Holding Data
- Customer Demographics
- Point of Sales throughput & Merchant Data
- Transaction Data
- Pre-Approved Offers for Customers
- Delivery datasets & Delivery Auto Logs
- Account level KPIs
Some of our current areas of interest include but are not limited to:
- Financial supply Chain: Identifying & classifying the inflow/outflow of transactions to identify potential customers
- Auto-reminder payment system: predicting the Customer’s next payment transaction
- Customer product recommendation model
Developing a recommendation model to determine what are the next products and services we should offer to the customer
Sample of actions for Service and Sales
- Service Request- Cheque Book Issuance, Biller Registrations and Auto pay
- Sales- Liabilities (SA, TD etc.) and asset products (credit card etc.)
- Analysis of POS Throughput and merchant data to identify any anomalies in the pattern of usage of POS terminals
Mentors

Rajat Kanwar Gupta
President
Business & Digital Technology
YES BANK

Mathangi Sri
Head Data Science, Phonepe

Gaurav Daftary
Quantitative Analyst
Moody’s Investor Services

Nitin Sareen
Head of Analytics Solutions & Delivery
Aditya Birla Group

Sayan Sen
Head – Data Labs
HDFC Life

Tanuj Taneja
Associate Director
Kvantum Inc.

Vaibhav Gupta
Director, EY

Ajay Ohri
Specialist Platform
SAPIENT AI PRACTICE

Farhat Habib
Director, Data Sciences
Inmobi

Nimilita Chatterjee
Sr. Vice President-Data & Analytics
Equifax

Subhabrata Sinha
Assistant Vice President, Insurance Analytics Genpact

Ananda Rao Ladi
Chief Learning Officer
Edureka

Hindol Basu
CEO, Actify Data Labs

Subrat Panda
Principal Architect, AI Technologies
Capillary Technologies

Subramanian M S
Head of Analytics
bigbasket.com

Ratnakar Pandey
India Head Analytics & Data Science

Saurav Gandhi
Director – Credit Risk and Data analytics – PayU

Mukesh Jain
Vice President & Head – Data Technologies
Capgemini

Biswa Gourav Singh
Lead ML Engineer, Capillary Technologies

VARTUL MITTAL
Technology & Innovation Specialist

Yogesh Joshi
CIO
Kores India

Arpit Agarwal
Associate Director, Data Science Zoomcar

Rohit Pandharkar
Head, Data Science & AI, Mahindra Group

Saurav Kaushik
Data Scientist
Uber
TOP 50 TEAMS
Altered_Carbon | Oracle | FinanceDataDons | DataNinjas | 2strat_1Inclnd |
Spartan4 | Random | Icube4analysts | TheBeginner | QuickSilver |
Data_scientist | Simpleworksai | Thomas_Bayes | AI_GLMatrix | Artis |
Asdfg | KRYPTON | High_Flyers | MesonLabs | RSDATA |
FakeData2018 | Insight | Random | Reboot_Rebels | team_prayaas |
Capillary_Tech | Earthing_123 | DataVirtue | Zessta | Greenity_Team |
reverse_atlas | Bacers | Sparks24 | SegFault | DJANGO_UNCHAIND |
Zsers | Team_Billa | Green_Peace | GSA07 | Type_2_Error |
HackNGo | FishersFour | S_V_D | Excelites | 5thQuantile |
data_exploders | data_warrior | MagnIeeT | Yes Data | Data Pirates |
AbAnalytics | The_Generals | Hashbreakers | The_Four | Agragens |
NLP_Rockers |
TOP Solutions
Oracle
The team has worked on a ‘master product’ which creates a single 360 degree view of every retail customer , and also provides customized product and service recommendations for every individual customers – (including product propensity/predictive service delivery/service resolution etc
To create the ML recommendation engine they have used individual and cluster wise product propensity and a cluster correlation technique. The solution will allow the bank to provide customized and predictive products and services to each and every customer, as well as allow sales managers to cluster their clients based on the propensity model
Insights
The team sas developed a unique model basis , using a modified version of Shapley’s value – called Shap Value to create a customer – product/service propensity model basis product holdings/demographics/transactions. For eg. the propensity of a customer with 2 FD holdings towards an MF investment. This model combined with Team Oracle creates a comprehensive Next Best Action model
Finance Data Dons
Product 1 : The team has created a data model which creates a personal finance management tool for every customer using transaction patterns as a base. The application will have the ability to predict and classify transactions as well as smart investment advisory once implemented
Product 2: The model will help optimizing Merchant relationships – both online and offline retailers. The team has used ML to cluster POS transactions and identify anomalies in transaction volume and volume. This will help to increase POS/gateways, identify new merchants, provide merchant offers
Django Unchained
The team has created an AI based application for relationship managers (sales representatives) of the bank which will enable them to measure share of wallet for the bank for every retail customer, predict attriction as well as provide customized products/services
Billa
This team has created a master algorithm which can form the basis/foundation of other data models. Basically, this model which takes a cue from representation learning helps to tag, classify and then sort data in a much quicker and innovative way. Almost 70% of the time spent by any data scientist on creating a working prototype goes in cleaning, tagging and classifying data. This model will help accelerate that process and remove manual involvement in the same.
Reverse Atlas
The team has worked on a unique ML algorithm (which finds usage on streaming sites) which helps identify customer relationships basis transactions and creating relationship trees for every customer basis transactions. This would help identify and target new customers with exact products/solutions/offers as well as deepen current relationships
This almost creates a social media like network using Financial transactions and demographics as the base. This is the first time this algorithm which finds usage in streaming sites will find an application in the financial services space.
NLP Rockers (TCS)
Uses NLP and clustering methods to convert email service requests into service tags across the entire customer bases and subsequent clustering and prioritization. The model then uses predictive analytics to create customer clusters and predict service requests per cluster
Zessta
This product works on lowering customer attrition using a clustering algorithm which identifies customers most prone to attrition.This product works on lowering customer attrition using a clustering algorithm which identifies customers most prone to attriction also measures the affinity/non-affinity of customers to products to help predict next best action for customers for both dormant and active customers
Avensis
Chat bots have been quicker and better but a missing link has been customization. This solution will help YES BOT provide more customized responses to customers/non customers using demographic and account data, while also providing regular services like payment reminders/EMI tracker and relevant investment notification
Meson Labs
This product takes a unique approach to managing finance and investment using neural networks to create human like learning as well as use Google adwords to help classify transactions since transaction markers are often commonly known brands or words. This helps create a relevant map of all transactions for individual customers, comparison of spends and investments and accordingly provide advisory”
Prayaas
The team has worked on 2 solutions, the first one helps manage and predict individual customer deliveries like cards/statements/tax statements/cheque books – providing proactive service, reducing human involvement and as a result reducing service requests
Data pirates
The model provides an alternative method to score and profile customers , pooling in LinkedIn APIs and other external APIs to help score customers who are currently not scoped by CIBIL
GSA07
A major problem that bank customers face is to connect to the bank to avail a service, address any queries or resolve an issue. To address this concern this model will put in place which anticipates a customer’s concern based on his demographics and transaction data and allows the bank to reach to their customers with a solution before the customer knocks at their door with a problem.
Fakedata
The model uses machine learning to create a customer satisfaction or ‘happiness index’ , and basis affinity of customers to products or brands (basis transactions) – provides customized offers/offering as well as enables more nuanced and targeted marketing
Data Ninjas
Team has worked on use cases for properly classifying merchants based on there business category(Wallet, E.Commerce, Online Shopping, etc.) and then using the result to get to know the affinity of a customer segment with a particular merchant.
For Merchant classification, they used 2 approaches, unsupervised clustering approach to cluster merchants on correct spelled centroids and second approach to hit Google search API and doing POS tagging to cluster based on Nouns and adjectives. For checking user affinity they used RFM model.
TOP Solutions
1. Data Wizards – Team Oracle
2. Data Acers :
a. Team Billa
b. Team Insight
c. Team Finance Data Dons
d. Reverse Atlas & Django Unchained
3. Data Pros
a. Team Seg Fault
b. Team WLP Rockers
c. Team Data Ninja & Zessta
d. Team Data Pirates & GSA 07
4. Data Champs – Team Insight
WHY PARTICIPATE?
Face the real-world Challenges
Access curated datasets (anonymized) and design your data models efficiently
Network with the community and have fun
Connect with like-minded data enthusiasts: explore and grow as experts together.
Mentorship
Acces to mentorship by industry experts in the field of Data Science, Big Data, FinTech and other emerging technologies.
Chance to work with YES BANK
Top 10 teams to get a working project with YES BANK to implement their data models
Win Cash Prizes
- Data Wizard – INR 5 Lakhs (1 Winner)
- Data Acers – INR 2 Lakhs Each(4 Teams)
- Data Pros – INR 1 Lakh Each(4 Teams)
- Data Champs – INR 1 Lakh (1 Student Team)
Unlock Access and Credits
All top 50 teams to get free AWS credits, access to Datagiri meetups and the opportunity to work with Cloudera tools
WHO CAN PARTICIPATE?
We’re inviting data scientists, analysts, AI/ML engineers, developers, startups, students, and anyone who is interested in working on Curated Banking Data* to join us for the journey to transform the ‘Future of Banking’ by building data-driven solutions.
We’re inviting data scientists, analysts, AI/ML engineers, developers, startups, students, and anyone who is interested in working on Curated Banking Data to join us for the journey to transform the ‘Future of Banking’ by building data-driven solutions.
HOW TO PARTICIPATE?
Step 1: Click here ‘Join the Datathon’ – you will be led to our datathon partner – Skillenza’s page for the YES BANK Datathon
Step 2: Click ‘Register’ and enter details required to sign up
Step 3: Get your crew together and form a team – we recommend cross-disciplinary teams (max. 4 members) comprising Data Scientist, Big Data Engineer, Coder, Full Stack Developer to register
Step 4: Once completed, you will get a confirmation mail from Skillenza. Happy data diving!
Datasets & Use Cases
Top 50 teams get access to curated banking data*
- Service Tickets & Leads
- Customer Product Holding Data
- Customer Demographics
- Point of Sales throughput & Merchant Data
- Transaction Data
- Pre-Approved Offers for Customers
- Delivery datasets & Delivery Auto Logs
Account level KPIs
Some of our current areas of interest include but are not limited to:
-
- Financial supply Chain: Identifying & classifying the inflow/outflow of transactions to identify potential customers
- Auto-reminder payment system: predicting the Customer’s next payment transaction
- Customer product recommendation model
Developing a recommendation model to determine what are the next products and services we should offer to the customer
Sample of actions for Service and Sales
-
- Service Request- Cheque Book Issuance, Biller Registrations and Auto pay
- Sales- Liabilities (SA, TD etc.) and asset products (credit card etc.)
- Analysis of POS Throughput and merchant data to identify any anomalies in the pattern of usage of POS terminals
Mentors

Rajat Kanwar Gupta
President
Business & Digital Technology
YES BANK

Mathangi Sri
Head Data Science, Phonepe

Vaibhav Gupta
Director, EY

Hindol Basu
CEO, Actify Data Labs

Biswa Gourav Singh
Lead ML Engineer, Capillary Technologies

Ajay Ohri
Specialist Platform
SAPIENT AI PRACTICE

Subrat Panda
Principal Architect, AI Technologies
Capillary Technologies

VARTUL MITTAL
Technology & Innovation Specialist

Gaurav Daftary
Quantitative Analyst
Moody’s Investor Services

Farhat Habib
Director, Data Sciences
Inmobi

Subramanian M S
Head of Analytics
bigbasket.com

Yogesh Joshi
CIO
Kores India

Nitin Sareen
Head of Analytics Solutions & Delivery
Aditya Birla Group

Nimilita Chatterjee
Sr. Vice President-Data & Analytics
Equifax

Ratnakar Pandey
India Head Analytics & Data Science

Arpit Agarwal
Associate Director, Data Science Zoomcar

Sayan Sen
Head – Data Labs
HDFC Life

Subhabrata Sinha
Assistant Vice President, Insurance Analytics Genpact

Saurav Gandhi
Director – Credit Risk and Data analytics – PayU

Rohit Pandharkar
Head, Data Science & AI, Mahindra Group

Tanuj Taneja
Associate Director
Kvantum Inc.

Ananda Rao Ladi
Chief Learning Officer
Edureka

Mukesh Jain
Vice President & Head – Data Technologies
Capgemini

Saurav Kaushik
Data Scientist
Uber
TOP 50 TEAMS
Altered_Carbon | Oracle |
FinanceDataDons | DataNinjas |
2strat_1Inclnd | Spartan4 |
Random | Icube4analysts |
TheBeginner | QuickSilver |
Data_scientist | Simpleworksai |
Thomas_Bayes | AI_GLMatrix |
Artis | Asdfg |
KRYPTON | High_Flyers |
MesonLabs | RSDATA |
FakeData2018 | Insight |
Random | Reboot_Rebels |
team_prayaas | Capillary_Tech |
Earthing_123 | DataVirtue |
Zessta | Greenity_Team |
reverse_atlas | Bacers |
Sparks24 | SegFault |
DJANGO_UNCHAIND | Zsers |
Team_Billa | Green_Peace |
GSA07 | Type_2_Error |
HackNGo | FishersFour |
S_V_D | Excelites |
5thQuantile | data_exploders |
data_warrior | MagnIeeT |
Yes Data | Data Pirates |
AbAnalytics | The_Generals |
Hashbreakers | The_Four |
Agragens | NLP_Rockers |