Session on "Use Cases and Risks of ML in Capital Markets" | 23rd Dec at 4pm Hi everyone! The AI and Risk Mitigation project is well underway and for the third session, we will be joined by Rachna Maheshwari, Associate Director at CRI… more
The 2023 Monsoon edition is curated by:
- Nischal HP, Vice President of Data Engineering and Data Science at Scoutbee. Nischal curated the MLOps conference which was held online between 23 and 27 July 2021.
- Sumod Mohan, Founder and CEO at AutoInfer. Sumod curated Anthill Inside 2019 edition, held in Bangalore on 23 November.
- AI and Research - covers research, findings, and solutions for challenges on building models in various areas such as fraud detection, forecasting, and analytics. This track delves into the latest methodologies for handling challenges such as large-scale data processing, distributed computing, and optimizing model performance.
- Industrial applications of ML - covers implementation of AI in the industry, with more focus on the AI models, the issues in training, gathering data so, and so forth. ML is being used at scale in industries such as automotive, mechanical, manufacturing, agriculture, and such domains. This track focuses on the challenges in this space, as we see innovation coming out of these industries in the pursuit of using ML on a second-to-second basis.
- AI and Product - covers strategies for building AI products to scale and mitigating challenges. This track provides insights on incorporating AI tools and forecasting techniques to improve model training, developing a working model architecture, and using data in the business context.
There are three phases in the lifecycle of an application - research, application and aftermath of the application.
- Assess capabilities, determining the new frontiers for AI.
- Find a use for the application.
- Learn how to run it, monitor it and update it with time.
The three tracks at the 2023 Monsoon edition of The Fifth Elephant will cover this lifecycle.
The Fifth Elephant 2023 Monsoon edition will be held in-person. Attendance is open to The Fifth Elephant members only. Purchase a membership to attend the conference in-person. If you have questions about participation, post a comment here.
- Data/MLOps engineers who want to learn about state-of-the-art tools and techniques, especially from domains such as automobile, agri-tech and mechanical industries.
- Data scientists who want a deeper understanding of model deployment/governance.
- Architects who are building ML workflows that scale.
- Tech founders who are building products that require AI or ML.
- Product managers, who want to learn about the process of building AI/ML products.
- Directors, VPs and senior tech leadership who are building AI/ML teams.
Sponsorship slots are open for:
- Infrastructure (GPU, CPU and cloud providers) and developer productivity tool makers who want to evangelise their offering to developers and decision-makers.
- Companies seeking tech branding among AI and ML developers.
- Venture Capital (VC) firms and investors who want to scan the landscape of innovations and innovators in AI and who want to source leads for investment in the AI and ML space.
Transforming COD from a Risk to Growth lever using Machine Learning
Paying for deliveries using cash after the delivery is made is a popular mode of payment employed by customers transacting online for the first time or those that prefer to have more control, especially in emerging economies like India. While the cash (or pay)-on-delivery (COD or POD) option helps e-commerce platforms, for example in our food delivery platform, tap into new customers, it also opens up substantial risk in the form of fraud and abuse. A common risk mitigation strategy is to impose a limit on the order value (MPL - maximum purchase limit) that can be paid using COD. MPL is typically blunt (a single limit for a city or zip code) and set by business teams using heuristics and primarily from a risk-management-backward view.
Blunt MPLs are a one-size-fits-all approach which means we leave money on the table for customer groups where the limits are too strict and lose money on groups where they are lax. We need to balance the risk management and the customer preference angles simultaneously and dynamically.
We try to frame this as a constraint optimisation problem and then try to find solutions to this using analytical models as well as an uplift modeling-based approach.
In this talk, I wish to present the following:
- A brief understanding of the COD payment method and MPL and their implications in Indian e-commerce.
- An understanding of the system that determines the COD eligibility of our customers
- Mathematical Formulation of the MPL determination problem
- An overview of the analytical and ML model-based solutions we built at Swiggy
- A view of the real-time inference framework
- Some experiment results and future scope