Telangana AI Academic Grand Challenge

About T-AIM

Telangana AI Mission (T-AIM), powered by NASSCOM has been established by the Government of Telangana with a vision to position Telangana as a global hub for Artificial Intelligence and foster social innovation. To encourage innovation within the AI community the ITE&C Department’s Emerging Technologies Wing and T-AIM actively conduct various initiatives throughout the year.

Objective of the challenge

To foster the entrepreneurial spirit amongst the students, T-AIM in collaboration with Wells Fargo is seeking applications for Academic Grand Challenge.

In this challenge, student teams from colleges across India are expected to build solutions for two areas. One involves building a futuristic, pure digital bank that integrates modern technologies to enable banking wherever and whenever. The other will require building a predictive analytics model to estimate if and when the financial markets will crash during this year.

Rewards

The following Rewards are available

Grand Prize
Winner

A total sum of Rs. 1,00,000 will be awarded.

Finalists

2nd Prize

A total sum of 50,000 will be awarded

3rd Prize

A total sum of 25,000 will be awarded

Top 20

Merchandise/Gift Vouchers will be awarded to the 17 other teams that made it to the top 20 positions

Use Case

The Neo Bank

Business Context

As future generation bank customers (Gen Z) use more and more digital services and products, it is important that banking should be available at their fingertips, and it should integrate well into their life.
It is imperative that pure digital banks can tap into these customers well. However, it is important for these banks to provide all services that offline banks provide. This requires the neo banks to be more open to integrations with various merchants and other institutions to provide services and products seamlessly. These banks also should innovate constantly on par with the latest technology trends like metaverse; block chain etc., to be relevant for Gen Z. This will also benefit the banks to save cost.

Desired Outcome

An innovative and viable solution using emerging technologies like Artificial Intelligence (AI) to build a futuristic pure digital bank, which is highly available, integrates with modern technologies, and enables the customers to bank wherever, and whenever.

Use Case

The Asset Bubble

Business Context

In a largely placid market for the most part in the prior years, we are staring at a stretch of considerable volatility in the financial markets. Now more than ever, investors need a good plan to move forward, and what good is the plan without the predictive power of analytics?

Desired Outcome

Help the investors by building a predictive analytics model or providing actionable analytical insights to this problem.
Output should be a confidence score on whether stock market will crash in 2022 or not and if it does find an approximate time interval in which this can possibly occur

Entering/ Submitting Your Solution

To submit your Solution and enter the Challenge on or before the Challenge Submission Deadline you must do the following:

Register for the challenge. To register please submit your details here: https://neohire.io/hackathons/nasscom-academic-grand-challenge/
Once you register, to start submitting your plan login to: https://nasscom.examly.io/login with your credentials. Navigate to Menu > Hackathon > Use Case Name > Access all the Documents > Submit your Plan
Contact taim@nasscom.in for any clarifications

Results and Winning Criteria

The teams will be judged based on approach, technique, and result.

Teams will be given six (6) weeks to work on the use-case
Teams will have to submit the results, approach, and code to be presented to the jury for evaluation
The winning criteria will be a combination of qualitative and quantitative methods to solve the problem
The decision of the jury will be final and binding.

Ranking Criteria (Parameters and Weightage)

Approach
Technical
Results

This consists of the description of how the problem statement was approached. Assumptions made. Understanding of problem statements. suggestion on how the solution can be taken to next level with the use of data/algorithms.
Weightage: 40%

This consists of code quality, use of data, scalability of the solution, use of standard libraries, algorithms used.
Weightage: 30%

This consists of accuracy of the solution by comparing it with unseen datasets
Weightage: 30%