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 >> >> >> >>
