Blog - Page 86 of 346 - MicroSave Consulting (MSC)

Ten stories of hope this year

Posted on January 6, 2021December 30, 2023 by MSC

It has been a grim year for us all. Yet 2020 showed us the sheer resolve and strength of low- and moderate-income communities as they battled the effects of the pandemic. And now, we can all look to the new year with hope. Here, we have curated the stories of 10 flagship projects where MSC’s work has had a substantial impact to improve the lives of LMI communities across Africa and Asia.

MFIs and agents who serve 25 million-plus customers used MSC’s informative comic booklets on coronavirus that focus on MFI client and staff, Self Help Group and CICO agents to reduce infection and death across more than 15 countries across three continents.
We worked with the Ministry of Home Affairs (MHA), Government of India (GoI) to monitor the response and readiness of state governments to address COVID-19. This enabled the government to help laggard states replicate good practices and plug weaknesses, and provided inputs to design the overall USD 285-billion relief package. See our fourth report and the accompanying dashboard. To gauge the effectiveness of the portion of the overall package (worth USD 23 billion) focused on the social safety net beneficiaries, we conducted two demand-side telephonic surveys of 5,000 households in 18 states. This provided inputs to the government to optimize how it implemented the package.
The FinTech committee of the Central Bank of West African States (BCEAO) has utilized recommendations from our six-country Inclusive Fintech Study to make informed decisions on upcoming regulatory text for sandboxes and support the states’ National Financial Inclusion strategies.
The success of the Pathways to Enhancing Financial Inclusion (PEFI) program has prompted the Department of Financial Services (DFS) to roll it out in 112 districts across India. This will have a positive impact on the financial lives of 161 million-plus beneficiaries, including more than 80 million women.
On behalf of UNCDF, we helped the Uganda National Planning Authority to develop the Uganda Third National Development Plan (NDP III – 2020/21 -2024/25) on the mapping and integration of ICT interventions into the different sectors of the Ugandan economy.
The Financial Inclusion Lab has nurtured 27 early-stage startups across India in three cohorts thus far. Cumulatively, these startups have already served 29 million individuals and raised more than USD 14.5 million in funding. It has also helped 6,900 micro, small, and medium enterprises (MSMEs) scale their businesses, and created 16,000 livelihoods in tier-2 and 3 cities and rural towns of India.
MSC led the “Awareness, Communication, and Outreach” working group of the Bill & Melinda Gates Foundation’s Global Situation Room response to COVID-19. The working group produced valuable tools to optimize communication of emergency support programs.
In collaboration with the National Financial Inclusion Council of Indonesia (DNKI), we concluded a study to assess existing KYC practices adopted by financial service providers in the country. This has been a part of MSC’s ongoing support to the Government of Indonesia in implementing an electronic KYC (eKYC) process using the national ID database.
Countries in West Africa, with their large inflow of remittances, offer opportunities to create cash-to-account services linked to demand-driven value-added services. This report highlights barriers and opportunities in the use of formal remittance services.
With support from FSDU, MSC worked with the FinTech Service Providers Association (FITSPA) of Uganda to develop a code of conduct to guide the development of a robust and ethical FinTech movement in the country.

Responding to COVID-19: India country report

Posted on January 5, 2021July 27, 2023 by MSC

MSC conducted research to understand how the low- and middle-income (LMI) segment in India continues to respond to the COVID-19 pandemic. This report provides insights into the knowledge, attitudes, and practices of the segment during the pandemic. It also provides recommendations for policymakers and financial service providers to help the segment recover from the crisis.

“Respect the sentiment:” Using sentiment analysis to inform policy and regulation

Posted on December 21, 2020March 18, 2024 by MSC

According to a Techjury, we create 2.5 quintillion bytes of data every day on the World Wide Web, a substantial part of which is text. Much of this takes the form of product reviews, news articles, and even social media messages, which contain a plethora of information that could help decision-makers in designing better products, monitor existing products, etc. People now use social media platforms to share grievances and satisfaction about everything from products and people to policies and programs. We cannot overlook the potential to utilize the data generated from such platforms.

The number of smartphone users across the globe has increased to 4.66 billion, while 3.96 billion people now have a social media account. Every day, 92,000 articles and more than 2 million blogs are published. However, we cannot quantify or analyze this data through traditional means. This is where techniques like sentiment analysis play a vital role.

Sentiment analysis is an analysis of feelings, emotions, attitudes, opinions, and thoughts, among aspects behind data sourced from Twitter, Facebook, and newspaper articles, besides other sources. The five-step process involved in this analysis is detailed below:

From a business perspective, this technique can help an organization analyze the perception of customers about its brand and in turn drive business decisions, strategies, and objectives. It can also help conduct a competitor analysis and build up business intelligence. Moreover, sentiment analysis is a useful tool to measure the ROI of marketing campaigns, support customer service, and augment public relations.

Though sentiment analysis has established itself as an important tool for growing businesses, it is increasingly being used in other fields as well, including government regulations and policy decisions. Regulators have used sentiment analysis to generate quick consumer insights and monitor consumer grievances placed by consumers against various financial service providers. For example, FSD Kenya and Princeton University, in partnership with CitiBeats, collected and analyzed Twitter messages to identify tweets relevant to consumer protection and directed toward 29 different financial institutions in Kenya.

A similar analysis can potentially also help policymakers gauge the sentiments of citizens and experts toward government policies and initiatives. Several research papers have already provided examples of the potential use of this technique in policy evaluation.

MSC has used sentiment analysis to gauge public opinion in several of its studies to evaluate public policy. The two examples below highlight the applicability of sentiment analysis to derive insights into public policies.

In its recent research, MSC used sentiment analysis to understand the public perception of Aatmanirhbar Bharat or self-reliant India, a blanket program launched by the Government of India to help citizens cope with the pandemic. We used data from Twitter to conduct the analysis. We selected keywords from online social media analytics tools like Brand24 and Google Trends for the analysis. The graph and word cloud below provide a glimpse of the results:

Our analysis highlighted an overall positive response to the program among the target audience. The graph on the left shows various emotions analyzed from the tweets. The most common emotion was trust, followed by anticipation and joy. On the right, a word cloud depicts words that occurred most frequently in the tweet corpus. The most common word was “Atmanirbhar Bharat,” the name of the government program. Thus, sentiment analysis provided a quick insight into public sentiment around the government’s initiative.

2. MSC conducted multi-round, multi-country research to understand the impact of COVID-19 on the low- and middle-income (LMI) segment. As part of this research, we also wanted to understand the public sentiment around the Pradhan Mantri Garib Kalyan Yojana (PMGKY), a support program launched by the Government of India to help the LMI segment deal with the pandemic. For the analysis, we collected 80 newspaper articles from six leading dailies on the topic. The graphs below offer a glimpse of the results we achieved:

The program invoked an overall neutral response among the target audience. The most frequently used positive terms included food, relief, cash, and benefit, which indicate the satisfaction of the citizens with the benefits provided under the initiative. The most frequently used negative terms can be divided into two categories—those related to COVID-19, which included the words pandemic, infection, crisis, hit, and fight and those related to the on-ground implementation of the program, which included income, demand, and payment. The word “government” also held a negative sentiment, considering the context of the sentence where it was mentioned.

The two studies highlighted above can work as proof of concepts on how sentiment analysis can support policy evaluation through rapid feedback from a large pool of users to policymakers.

However, sentiment analysis must be used with caution. As we cannot choose our samples based on socio-demographic variables, a targeted analysis is not possible. Hence, some of the most vulnerable segments are excluded from this analysis, particularly those who do not have access to a smartphone, do not use social media, or are illiterate. Conducting sentiment analysis in regional and local languages is also a challenge due to the lack of well-established and robust algorithms that take into account the structure and nuances of languages other than English. Methods used to circumvent these roadblocks often compromise on the accuracy of the results. The availability of robust data on the topic of interest is another concern. The analysis requires sufficient source material online, such as tweets and articles, among others, to draw meaningful insights on a particular topic.

Regardless of these caveats, sentiment analysis can help businesses, policymakers, and regulators generate quick insights to inform decisions. It can also help develop an insightful, data-based marketing strategy for a business as well as any government program or policy. The early movers who adopt this technique will likely reap substantial benefits.

Data analytics credit: Mohak Srivastava, Pritam Kumar Patro, Mahi Balusu

A paradigm shift for fintechs: Why a coherent data strategy has become a business necessity

Posted on December 17, 2020December 20, 2023 by MSC

The increase in internet accessibility through human-to-human (voice, text chats), human-to-machine (internet searches), and machine-to-machine (Internet of Things or IoT) interactions has led to an explosive proliferation of digital data, with 33 zettabytes of data produced in 2018 alone – a number predicted to increase to 175 zettabytes by 2025 (1 zettabyte is 1 trillion gigabytes). And despite the fact that our digital footprints have increased vigorously, and various data-focused organizations have stepped in to map them, according to this 2016 Mckinsey article only 1% of the enormous amount of data that exists has been analyzed.

Meanwhile, the advent of machine learning and IoT has changed the way we look at this information. Companies are increasingly adopting data analytics to draw insights for their businesses. The analyzed information has helped upend customer expectations, while enabling businesses to optimize operations to unprecedented levels.

This explosion in data and analytics technologies can provide a decisive competitive edge to fintech companies. Below, we’ll explore how they can leverage these tools to their benefit – and the benefit of their customers.

Why should fintechs value data ?

Data and analytics can allow fintechs to combine innovative business models and technology to enhance financial services. Fintechs, owing to the highly digital nature of their work and the burgeoning mobile access of their customers, can access large pools of customer information as valuable data points. Their ability to harness these data points to gain insights and drive business decisions can help them outrun their competitors. To that end, data analysis applications can be used to generate insights for diverse elements of fintech business models. Here are a few areas in which data analytics can play a critical role.

Some early adopters among fintechs have already made use of their organizational data to gain a competitive edge. Zest AI, a U.S.-based technology firm, uses machine learning to accurately predict a borrower’s ability to repay. Microcred (rebranded as Baobab) offers financial services to unbanked people in Africa by doing consumer analysis locally using cloud business intelligence. And Coverfox, an Indian fintech, uses insights derived from AI algorithms to allow users to compare and choose from a range of plans across top insurance companies.

MSC, as a partner of IIM Ahmedabad’s CIIE.CO’s Financial Inclusion Lab, has helped fintechs grow their businesses through capacity building and technical assistance. Our data domain has been working alongside partnering fintechs to build their data strategies, and to integrate analytics in their business decision processes. Based on our experiences so far, we see that many fintechs fail to fully embrace the vast potential of data. The prevailing wisdom is to keep heads down, maintain the status quo and rely on a gut feeling about the market – often driven by qualitative data and anecdotal information – in order to dedicate maximum time to managing the growth of the business. However, while this strategy may yield limited results and generate short-term tactical gains, it is inherently conservative and rarely leads to significant and sustainable growth.

The three stages of a coherent data strategy

Our experience working with fintechs indicates that they need to take a bolder approach, embarking upon a journey of adopting data-driven ways of doing business. We can divide the journey into three different stages.

These three stages to creating a coherent data strategy consist of:

Robust and diligent data collection: For fintechs to leverage their organizational data, the first step is stringent data collection. This requires robust processes and quality checks to screen for missing or false information.

Creating proper data storage protocol: Once data collection has started, fintechs must implement effective standard operating practices regarding data storage. Maintaining a central database of all existing organizational data helps to ensure data privacy, and also allows fintechs to easily “slice and dice” the data for use in different business objectives.

Embracing statistically backed models: The end objective of this entire exercise is to use data to assist in business decision making. Hence, it is imperative that organizations use robust and accurate data modelling methods to help develop actionable insights.

Incorporating these three stages into a workable data strategy can be easier said than done. Quite a few fintechs use data primarily to monitor operations and track growth. The idea of converting data into insights to inform strategic business decisions is not clear to them. Similarly, fintechs with low understanding of data quality and completeness also suffer from under-utilization of data. This lack of understanding often leads them to collect and store data in non-analyzable formats, or to fail to integrate their different datasets to drive maximum potential. And even fintechs with a well-developed data strategy often struggle to move beyond descriptive analysis and delve into predictive or prescriptive analytics to guide their broader business strategy.

MSC’s work with our fintech clients has illustrated both the challenges these companies face in this journey, and the benefits they can obtain. For instance, we helped one fintech revise their credit scoring methodology by removing 75% of their parameters (which were redundant) to zero in on just 13 of them, while adding some important ones that they had overlooked. This made their credit scoring model robust, and reduced the overall cost of data collection and customer acquisition.

Another client was providing a blockchain platform for chit funds, and they wanted to use data to examine their customer retention. Though this fintech had a data strategy in place, the team was not integrating the different datasets to draw meaningful conclusions about their business processes. After a deep-dive diagnosis, MSC used unsupervised learning methods to develop a robust customer segmentation model for them. We also worked on integrating their data from segregated systems, including customer and chit fund payment details, to conduct this analysis for them.

Some fintechs are data-savvy and understand the value of their data, but lack the organizational prowess to leverage it to drive business decisions. MSC worked with one such fintech, which provides last-mile delivery to rural customers. We developed a decision tree model to identify determinants for high performance among its staff, to bolster its recruitment strategy. We also performed market basket analysis on their sales data to identify product combinations for cross-selling, thus strengthening their sales strategy.

These experiences have generated three key realizations:

Data as a key driver of competitive advantage in business is here to stay, and the sooner players adopt it, the better they can run their businesses.
Data can support all business-related decisions, ranging from customer acquisition to risk assessment and employee recruitment.
Fintechs need to assess their current data practices, identify any shortcomings, and build a holistic data strategy to support their business growth.

The potential of data cannot be ignored in today’s world. Organizations that quickly adapt to this new reality will not only ensure their survival, they will flourish and continue to grow. Hence, it is imperative that fintechs understand the value of data collection, storage and analysis, to enable them to leverage this invaluable tool in the near future.

This blog was also published on Next Billion on 16 December 2020.

The Landscape of Climate and Disaster Risk Insurance (CDRI)

Posted on December 17, 2020February 14, 2024 by MSC

Data from countries in Asia, South Asia, and the Pacific suggests that disasters caused losses worth USD 140 billion in the past decade. Of these, only USD 20 billion, or a mere 14.2% were insured across all sectors. While countries have well-defined disaster response and management frameworks, risk transfer through insurance needs systematic adoption. MSC, with support from GIZ, conducted a landscape study to understand the status of climate and disaster risk insurance in South and Southeast Asia and Oceania. Our report provides recommendations for policymakers and financial service providers to mitigate and manage disaster-induced losses.