Hi Lalit, I would be happy to explore contributing to the whitepaper.
Best, Aashish On Mon, Mar 1, 2021, 12:22 Lalit Mohan S <[email protected]> wrote: > Thanks James for your feedback. > > It is a great idea to have a white paper. I will look forward to members > that would like to contribute as well. > > We will factor your suggestion on the priority list. > > Regards > Lalit > > On Sun, Feb 14, 2021 at 11:57 PM James Dailey <[email protected]> > wrote: > >> Lalit, Ed and AI/M team - >> >> Nice job on: https://mifosforge.jira.com/l/c/8KpdQP4a >> >> This is a nice compilation of potential items for AI/ML on the >> Fineract/Mifos stack. >> >> #1) I think it would be great to turn this into a White Paper for the >> fineract/mifos communities. The White Paper should also address where in >> the stack changes are needed. I believe that "data pools" (or similar) >> would need to be created outside of the operational datasets, and that may >> require some changes to the database and data extraction strategies. I >> guess there are many issues that need to be addressed and reasons to move >> these functionalities forward. Making that case formally and determining >> criteria for priorities, seems like a good step. >> >> #2) In the absence of an overarching framework for evaluating priorities, >> my gut instinct on priority: >> A) help with operational risk (e.g. Fraud, Portfolio risk factors, >> projection of on lending or capital requirements ) ; >> B) improve product reach (e.g. more Credit Risk scoring); >> C) make operations more efficient . >> (in that order) >> >> #3) I would add one Use Case, incorporating into the money management use >> case the concept of multi-currency and market fluctuations for reducing >> exposures. There are two applications of this. *ONE* is that many >> Financial Institutions (including Microfinance Orgs) take out debt for >> on-lending in dollar or euro accounts and have to contend with repayment in >> local currencies, thus requiring careful tuning of their interest rates >> charges to consumers and other currency hedges. *TWO* some financial >> institutions are participating in remittance schemes where FX exposures are >> non-negligible, and some FIs would anticipate being in an intermediary role >> in those flows if they could. >> >> #4) Datasets - I only have some suggestions - areas of inquiry: >> A) better internal data: part of the issue with fraud detection is >> finding the right sort of pattern recognition - and that requires looking >> at a lot of operational data (timing of loans, amounts, unusual transfers, >> login from devices, etc) and then flagging potential cases for human >> review. Algorithms can then be trained. >> B) economics for products and risk of portfolio require exploring >> available proxy data. The "people's economy" - i.e. the economy lived by >> the poor or semi-poor often is obscured from official statistics. Call Data >> Records (CDR) were an early area of exploration but for obvious reasons the >> mobile networks are not keen to share that. Consumer spending data for >> things like kerosene, wood for cookfires, LPG, motorcycles, bikes, solar >> lanterns, may be a good way to go if available. Commodity prices are useful >> in anticipating consumer spending reductions in other areas (i.e. the price >> of rice goes up, spending for other consumables goes down ... in theory) >> >> I hope all that helps. >> >> Thanks, >> @[email protected] <[email protected]> >> >> >> On Wed, Jan 20, 2021 at 10:26 PM Ed Cable <[email protected]> wrote: >> >>> Hi everyone, lalit and the other members of our AI and ML working group >>> have been documenting the various AI use cases that we could possibly focus >>> on as part of the community-wide AI for all strategy and roadmap. >>> >>> We would like to get the community's feedback on these use cases and >>> which ones you might like to see prioritized, and whether or not you've got >>> data sets to help with some of the use cases that are focused upon. >>> >>> Could you please review https://mifosforge.jira.com/l/c/8KpdQP4a? >>> >>> >>> And share your comments via email on the following (especially the >>> priority of which use cases to focus on): >>> >>> a) Addition/modification of use cases >>> b) Priority of the use cases >>> c) Available datasets and any other inputs on federated learning >>> >>> >>> The working group meets every other Friday for those interested in >>> participating. >>> >>> Best wishes, >>> >>> Ed >>> >>> >>> >>>
