Explore untouched data sources and make them harvestable for the capital markets world.
The biggest opportunity for actors in the financial sector in this decade comes from the signals buried in the data generated by the digital economy. That so-called alternative data is the deepest, least utilized source of alpha in the world today. But, the data is often not readily accessible and less structured than traditional sources of data.
Novel data-driven approaches using artificial intelligence and machine learning enable market participants to automatically collect, normalize, and analyze the data sets on a greater scale. As a participant in our Hackathon, you can build cutting-edge tools for businesses to integrate poorly structured data in a fully automated manner. Run your sophisticated Natural Language Processing algorithm, test your dark Random Forest or do a fancy Sentiment Analysis. The ultimate goal is a better understanding of alternative data and to make it ready for its usage in the financial sector. Such gained insights may help market participants to understand e.g. the relationship between the formulation of investment risks or stock market trends. This could reduce information asymmetries and thus contribute to the efficiency and stability of markets.
Smart debit card for kids that parents manage from their phones
Kids and teenagers all over the world are forcing the banking industry to rethink their client service model. The traditional model prides itself on building personal relationship and trust with its clients, typically over a period of time. However, the next generations have very specific needs and preferences for technology-based interaction and instantaneous response.
We invite you to help shape the future of debit and credit cards by contributing your ideas on how to best engage, gain, and sustain the interactions with kids and teenagers and, at the same time, help improve their financial education. Your ideas may include the development of a hybrid tool, innovative concept of a multi-channel platform, or an entirely new comprehensive, personalized debit card; you name it.
Modex BCDB – Decentralize personalized data within the banking framework and move ownership to data owner
Technology to use
Blockchain as a database (BCDB) developed by Modex
In the crediting business of banks, there are legal obligations that banks must comply to for their periodic reporting to the central bank, which includes (among others) credit information. The national bank of Romania sustains an institution which was created at the initiative of 20 banks, named “Biroul de Credite” (in translation “The Credits Office”), which has a database of credits related information given by banks (who, for what period, are there delays).
Create an application / an ecosystem of applications (“tool”) that uses blockchain’s powerful attributes through BCDB technology, where 3 types of actors can exist: natural/legal entity client of a bank (“customer”), private banks, and the national bank. All information related to crediting (credit data, and delays) will be reported by private banks in that tool in the name of the customer. The national bank can access data at any point in time (assuming the law requires it). Any other actor who wishes to see credit information of a customer will access this tool and a permission to see data will be asked from the customer (which he may give or not, according to regulations as well).
– usage of a powerful secure/decentralized tool –> data is safe, it can never be changed and there is a minimum possibility of loss
– the customer knows at all times what happens with his data (who sees it, when and why) –> move data ownership to the real owner
– the customer knows what his credit data actually is (today the procedure to retrieve what kind of information does “Biroul de Credite” have about you is heavy) –> data owner knows his data
– there is no need (anymore) for the customer to go to the bank to sign a paper agreeing to consulting “Biroul de Credite”; he can approve from an application (mobile); if the customer does not have access to an application, only then he can go to the bank in person and sign a paper (which can also be saved in the tool by the private bank) –> friendlier banking environment, moving away from the requirements of having to be “in-person” at the bank
The purpose of this new tool is to replace “Biroul de Credite” with a powerful secure technology, and to allow customers to be owners of their own data.
Build a p2p micro loans platform to disrupt the banking industry
A regular person who wants to buy the latest smartphone, but doesn’t have the means or doesn’t want to pay upfront the whole sum, is forced to resort to the banking system for a personal needs credit. On the other hand, there are plenty of people who keep their savings in a bank account, often for interest rates below inflation, effectively losing money. Help build a marketplace where borrowers and lenders can meet and help each other.
Create a web application that will automatically match offers from lenders with requests from borrowers.
To place offers, a lender will have to set interest rates for one or more standard periods (e.g. 3 months, 1 year, etc) and the minimum credit score accepted from a borrower. All loans will be in USD and will have a fixed interest rate. When submitting a request, the borrower will have to specify the amount they want to borrow and the period for the loan. The platform should aggregate the best offers that fulfill the request and present a final offer to the user. Borrowers will have a credit score that should adjust positively with every successful payment and negatively with every delayed payment or default on the loan.
Build an Anti Money Laundering (AML) transaction/alerts monitoring system for the banking sector
There is a need to refine how the alerts are generated, reviewed and closed by the Bank’s teams since there is a regulatory requirement that suspicious transactions are to be reviewed, closed and / or reported in 48 hours and, in most cases, this is a time-consuming process. The current algorithms for alerts generation do not always prioritize the alerts to be reviewed by the Bank.
Using Machine Learning algorithms, create a tool that will generate and prioritize alerts of suspicious transactions in the banking industry.