Re: Feedback Requested: Use Cases and Focus Areas for AI for All Roadmap

[Lalit Mohan S <sl...@gmail.com>]

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 <ja...@gmail.com>
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,
> @jdailey@apache.org <jd...@apache.org>
>
>
> On Wed, Jan 20, 2021 at 10:26 PM Ed Cable <ed...@mifos.org> 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
>>
>>
>>
>>

Re: Feedback Requested: Use Cases and Focus Areas for AI for All Roadmap

[Aashish Sawhney <sa...@gmail.com>]

Hi Lalit,

I would be happy to explore contributing to the whitepaper.

Best,
Aashish

On Mon, Mar 1, 2021, 12:22 Lalit Mohan S <sl...@gmail.com> 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 <ja...@gmail.com>
> 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,
>> @jdailey@apache.org <jd...@apache.org>
>>
>>
>> On Wed, Jan 20, 2021 at 10:26 PM Ed Cable <ed...@mifos.org> 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
>>>
>>>
>>>
>>>