Page 52 - ITU-T Focus Group Digital Financial Services – Technology, innovation and competition

ITU-T Focus Group Digital Financial Services
Technology, Innovation and Competition

activities. This section looks at key examples from this list and analyses their strengths, weaknesses, and
applicability as relevant to DFS.

3.1 Identification technologies

Establishing an individual’s identity during registration for DFS can be one of the most significant barriers to
adoption when trying to drive inclusion. The process of registration can be slow and cumbersome, and is often
impaired by low income and rural demographics being unable to meet qualification requirements. New and
emerging technologies alongside the FATF RBA present an opportunity to overcome these issues.

3.1.1 Unstructured and structured data analytics engines

The practice of analysing aggregated data in order to draw insight from personal information is gaining
momentum within the financial services industry. GO Finance in Tanzania leverages digital data to underwrite
loans for small and medium-sized enterprises . Konifo, from Mexico, is another example which uses credit
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algorithms based on alternative data sets to extend the same services .
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Although much of the hype surrounding the use of this technology is centred on data from social media, low
penetration among target demographics does not necessarily dictate a lack of usable information. Any existing
services that facilitate the potential for individuals to produce a digital footprint can be leveraged for identity.
Typically, platforms capable of establishing data attributes from alternative sources can be measured according
to a two factor criteria:

1. Their ability to capture and structure useful information from traceable interactions between individuals
and software.

2. Their ability to draw insight from the aggregated data they collect: including the relationship between
individual data attributes and links between the attributes of separate entities.

As connectivity improvements increase the scope of available data, the use of analytic engines within developing
economies will become a progressively valuable prospect. However, the integrity of the calculations used
in order to establish a level of assurance around a consumer’s identity is critical. It is unlikely that parallel
mechanisms for converting unstructured data into an identity will be the same. Therefore, regulators will be
required to establish complex benchmarks in order to satisfy international and domestic obligations on AML
monitoring and counter terrorism funding (CTF) prevention. Adequate protections around user privacy will
also have to be established.

In order to meet the demands imposed on regulatory authorities by this type of technology, parallel investment
in tools that allow regulatory compliance to be adequately monitored whilst also ensuring compliance with
privacy obligations is crucial if the technology is to achieve widespread acceptance.

Examples: Hello Soda Konifo 2
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1 http:// hellosoda. com/
2 https:// konfio. mx/

9 https:// cfi- blog. org/ 2015/ 10/ 13/ the- data- story- in- the- fi2020- progress- report- on- credit- reporting/ #more- 19663
10 https:// konfio. mx/

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