Hello Ed/James,

Sorry for the late reply, I'm traveling most of the time in different time
zones.

I agree with James, I had a couple of calls to collect ideas of how can
machine learning help us and my only concern was our access to the data and
how structured and classified is the data. If there will be a need to
annotate or map the data manually and what's the size of the data that we
can extract?

If anyone has an input here that would be very helpful.

Thanks,
Maha


On Wed, Apr 4, 2018 at 11:00 PM, James Dailey <jamespdai...@gmail.com>
wrote:

> Hi Maha -
>
> I've been spending some time thinking about this for some business areas
> adjacent to financial inclusion.
>
> In financial inclusion, and financial services in general, there are of
> course, many areas where some ML or even basic analysis would do a
> wonderful job.
>
> For Mifos I believe a key thing that is needed is the ability to extract
> data in meaningful structures (flattened in appropriate ways) and within a
> reasonable taxonomy of credit, accounting, and consumer terms.  Perhaps
> that means making data more easily discoverable via APIs.  I also suspect
> that permission and rights management to data - with screens for privacy
> (or anonymizing) are needed.
>
> Related, one concept I've come across is the ability for third parties to
> get the "results" of machine learning models without actually getting
> access to the data and to apply that in a kind of federated data approach -
> where the models are trained up and then "call home" with the results.
> This would imply that it is important for the data access APIs to be
> structured consistently and with backwards compatibility.
>
> For ML to work there needs to be some other "big" data sets related
> together.  You may be interested in a recent world bank conference where a
> bunch of new ML applications were introduced:  https://blogs.
> worldbank.org/developmenttalk/artificial-intelligence-
> economic-development-conference-roundup-27-presentations
>
> Happy to get on a call at some point.
>
> Thanks,
> James
>
>
> On Wed, Apr 4, 2018 at 10:45 AM Ed Cable <edca...@mifos.org> wrote:
>
>> Maha,
>>
>> I wanted to resurrect this thread and invigorate some discussion as we
>> have
>> a GSOC prospect, Lalit, considering a project focused around machine
>> learning. I've added him to this thread and going to also add the team at
>> BowPI so they can contribute to the discussion around what they've done on
>> the machine learning front to date.
>>
>> Ed
>>
>> On Thu, Nov 9, 2017 at 4:58 PM, Avik Ganguly <avikganguly...@gmail.com>
>> wrote:
>>
>> > Hi Maha,
>> >
>> > Welcome to the community! Cognitev looks like an exciting startup. The
>> > current developments going on in Fineract; i.e. the upcoming Fineract CN
>> > application probably makes this the right time to initiate this effort
>> as
>> > ML services usually needs to scale separately and the independence of
>> > choosing a separate tech stack for these services matters a lot.
>> >
>> > Adding a bit on one part that Ed wrote - "monitor ongoing credit risk".
>> In
>> > the microfinance space, the most recurring problem statement I have
>> heard
>> > from risk heads in India is collusion by loan officers to meet targets
>> with
>> > / without ringleaders which is addressable through early warning
>> systems.
>> > Looking forward to working with you.
>> >
>> > With best regards,
>> > Avik Ganguly.
>> >
>> > On Wed, Nov 8, 2017 at 9:02 PM, Ed Cable <edca...@mifos.org> wrote:
>> >
>> > > Hello community,
>> > >
>> > >
>> > > I wanted to introduce to you a new Mifos Initiative volunteer, Maha
>> Refai
>> > > <https://www.linkedin.com/in/maharefai/>, who will be reaching out to
>> > > members of the community to document their requirements around credit
>> and
>> > > risk analytics using artificial intelligence, machine learning and big
>> > > data.
>> > >
>> > > Maha Refai is currently the co-founder and Head of Product at
>> Cognitev, a
>> > > startup based in UAE and Egypt using artificial intelligence to
>> automate
>> > > digital marketing. After receiving her bachelor’s in Computer Science
>> she
>> > > spent the past ten years primarily in product and project management
>> in
>> > the
>> > > entertainment industry while also getting her MBA.
>> > >
>> > >
>> > > She will be reaching out to some of you directly and documenting her
>> > > findings on the wiki. But if you'd like to be part of this
>> requirements
>> > > gathering process, please reply to this thread and we'll set up a
>> call.
>> > >
>> > > Based on her knowledge and expertise of artificial intelligence and
>> > machine
>> > > learning, she is going to help  develop a product management strategy
>> for
>> > > both the current Apache Fineract platform and the upcoming Apache
>> > Fineract
>> > > CN application framework that identifies and prioritizes what are the
>> > > necessary microservices and modules that are part of the framework or
>> > > integrated with it to facilitate the capture of data, the analysis of
>> it,
>> > > and the ability to make decisions upon the data.
>> > >
>> > > In short, volunteer will help identify how these predictive analytical
>> > > tools along with big data can help make Mifos and Apache Fineract more
>> > of a
>> > > loan origination system, as well as means of developing a credit
>> score to
>> > > manage and monitor ongoing credit risk. You can see below for some
>> more
>> > of
>> > > the specific tasks she’ll be undertaking.
>> > >
>> > >
>> > >    -
>> > >
>> > >    Research on existing open source tools or libraries that could be
>> > >    integrated for machine learning such as weka.
>> > >    -
>> > >
>> > >    Research on alternative credit scoring tools that are in existence
>> > >    -
>> > >
>> > >    Interviews with existing community members to discuss systems they
>> > have
>> > >    implemented or how they’ve been able to do these data analysis
>> using
>> > the
>> > >    existing Mifos toolset.
>> > >    -
>> > >
>> > >    Interviews with existing community members who have desires to
>> > implement
>> > >    credit scoring capabilities and big data analytics including
>> > stewarding
>> > >    these conversations over our community mailing lists and
>> documenting
>> > >    requirements publicly
>> > >    -
>> > >
>> > >    Recommendations on a strategy of how to implement such features -
>> > either
>> > >    as core microservices or ancillary modules.
>> > >    -
>> > >
>> > >    User Stories on actual features to implement within the framework
>> or
>> > >    product.
>> > >    -
>> > >
>> > >    Brainstorm and list out the insight and information that could be
>> > >    analyzed so we can understand what data about the customer, their
>> > > behavior
>> > >    and their loans must be captured in the framework and platform.
>> > >
>> > >
>> > >
>> > > --
>> > > *Ed Cable*
>> > > President/CEO, Mifos Initiative
>> > > edca...@mifos.org | Skype: edcable | Mobile: +1.484.477.8649
>> <(484)%20477-8649>
>> > > <(484)%20477-8649>
>> > >
>> > > *Collectively Creating a World of 3 Billion Maries | *
>> http://mifos.org
>> > > <http://facebook.com/mifos>  <http://www.twitter.com/mifos>
>> > >
>> >
>>
>>
>>
>> --
>> *Ed Cable*
>> President/CEO, Mifos Initiative
>> edca...@mifos.org | Skype: edcable | Mobile: +1.484.477.8649
>> <(484)%20477-8649>
>>
>> *Collectively Creating a World of 3 Billion Maries | *http://mifos.org
>> <http://facebook.com/mifos>  <http://www.twitter.com/mifos>
>>
>

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