##About the 2019 edition:
The schedule for the 2019 edition is published here: https://hasgeek.com/anthillinside/2019/schedule
The conference has three tracks:
- Talks in the main conference hall track
- Poster sessions featuring novel ideas and projects in the poster session track
- Birds of Feather (BOF) sessions for practitioners who want to use the Anthill Inside forum to discuss:
- Myths and realities of labelling datasets for Deep Learning.
- Practical experience with using Knowledge Graphs for different use cases.
- Interpretability and its application in different contexts; challenges with GDPR and intepreting datasets.
- Pros and cons of using custom and open source tooling for AI/DL/ML.
#Who should attend Anthill Inside:
Anthill Inside is a platform for:
- Data scientists
- AI, DL and ML engineers
- Cloud providers
- Companies which make tooling for AI, ML and Deep Learning
- Companies working with NLP and Computer Vision who want to share their work and learnings with the community
For inquiries about tickets and sponsorships, call Anthill Inside on 7676332020 or write to firstname.lastname@example.org
Sponsorship slots for Anthill Inside 2019 are open. Click here to view the sponsorship deck.
Use cases of Financial Data Science Techniques in retail
Financial domains like Insurance and Banking have uncertainty itself as an inherent product feature, and hence makes extensive use of Statistical models to develop, valuate and price their products. This presentation will showcase some of the techniques like Survival models and cashflow prediction models, popularly used in financial products, how can they be used in Retail data science, by showcasing analogies and similarities.
Survival models were traditionally used for modeling mortality, then got extended to be used for modeling queues, waiting time and attrition. We showcase, 1) How the waiting time aspect can be used to model repeat purchase behaviors of customers, and utilize the same for product recommendation on particular time intervals. 2) How the same survival or waiting time problem can be solved using discrete time binary response survival models (as opposed to traditional proportional hazard and AFT models for survival). 3) Quick coverage of other use cases like attrition, CLTV (customer lifetime value) and inventory management.
We show a use case where survival models can be used to predict the timing of events (e.g. attrition/renewal, purchase, purchase order for procurement), and use that to predict the timing of cashflows associated with events (e.g. subscription fee received from renewals, procurement cost etc.), which are typically used for capital allocation.
We also show how the backdated predicted cashflows can be used as baseline to make causal inference about strategic intervention (e.g. campaign launch for containing attritions) by comparing with actual cashflows post-intervention. This can be used to retrospectively evaluate the impact of strategic interventions.
Importance of Survival Regression techniques in modeling events and their timings in finance (3 mins)
Discuss difference between event models and waiting time models (2 mins)
Showcase traditional survival models and discrete time survival models solved through ensemble learning and ANN. Compare and show advantages, disadvantages (3 mins)
Retail Use cases:
Product Recommendation using waiting time models: repeat purchase behavior as a function of waiting times between purchases and other factors (detailed showcase) (5 mins)
Customer Attrition models (quick overview) (1 min)
Inventory management using waiting time models and queuing theory (quick overview) (1 min)
Cashflow models for subscription based retail businesses, and how it can be solved using survival models (3 mins)
Causality analysis of Promotions/Campaigns using cashflow models
Sudipto has worked as predictive modeling expert in Actuarial Science domain in Insurance Industry (with Swiss Re, AIG) for 8+ years, and in Retail and Marketing Analytics domain (with Walmart Labs) for 3 years. He has built and productized various models hands on for Insurance business which have been used for Pricing, Reserving, Risk Management, Campaigning etc. Similarly he has built ML products for retail which has been used for Marketing, Customer Analytics, Cashflow Modeling, and Budget Allocation. Sudipto did his B Stat and M Stat degree from Indian Statistical Institute, Kolkata. Sudipto has used his expertise in Statistics, Economics, Finance and ML to ensure successful adoption of Statistics and ML in Insurance and Retail products. Sudipto has led a predictive modeling team as a Manager and Director for 3 years in AIG. He is a currently a Staff Data Scientist with Walmart Labs.
- presented a related topic in ICBAI 2018 conference in IISC, Bangalore. A scanned copy of Book of abstracts can be found here: