MSMEs – The backbone of the Philippines economy
MSMEs (Medium, Small and Micro Enterprises) segment is a large and heterogeneous segment consisting of micro, small, and medium enterprises. Different sub-segments have their characteristics and peculiarities, leading to different credit needs.
MSMEs form 99.6% of total businesses in the Philippines and employ around 62% of the country's labour force. However, their contribution to GDP growth is just 36%.
Do you wonder why? Why have MSMEs not been able to scale their business?
This may be attributed to various operational factors, viz.
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Most micro-enterprises are doing traditional trading activities and are still focused on the domestic market.
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They have nil or almost negligible exposure to global markets or value chains.
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Low adoption of technology in production processes and business communication further contributes to this low share.
Another primary detriment to the growth of MSMEs in the Philippines economy is the lack of bank financing for MSMEs. Their share of the bank's total lending portfolio has reduced from 11% in 2010 to about 6% in 2019. Covid-19 outbreak lent another shock to the dwindling MSME economy, with over 525,000 MSMEs in the Philippines closing down in May 2020. This also led to an increase in the unemployment rate, with around 4.6 million Filipinos being unemployed as of June 2020. As such, this share may have decreased further.
Government response to Covid-19 outbreak
The government offered the following relaxations for the sector:
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Expanded Pondo sa Pagbabago at Pag-asenso (P3) Program for Covid-19
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Covid-19 Assistance to Restart Enterprises (CARES) Program
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Helping The Economy Recover Through OFW Enterprise Startups (HEROES) Program
Bangko Sentral ng Pilipinas (BSP) has also allowed peso-denominated loans to MSMEs granted, renewed, or restructured post 15 March 2020 to be covered under alternative compliance of reserve requirement for the banks.
This helped MSMEs to survive the Covid-19 wrath, as:
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MSME borrowers were able to service their debt – Top 10 UKBs recorded loan collections of P174.8Bn from May to Aug 2020.
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Loan collections outpaced the P148.8Bn in newly-granted MSME loans for the same period.
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As of 26 November 2020, the banking system allocated Php134.8Bn loans to MSMEs for compliance with the reserve requirement, but still significantly far from the limit of Php300Bn set by BSP.
So, while MSMEs are looking for financial support for economic growth and recovery, banks are also being incentivised to lend out to MSMEs.
But then, why is there still a significant difference between the MSME funding requirement and the actual lending?
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Banks are walking the tight rope between Credit Quality and Business Growth.
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Banks willing to lend out, but only to the good MSMEs.
They continue to leverage digital technology to scan the credit profile, intelligent credit risk monitoring, automated credit origination, monitoring, etc., aiding recovery and growth towards MSME Lending.
Evolving lending systems and processes
Here is how the lending systems and processes are evolving:
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From manual operations subject to human bias to digital and automated operations with consistent implementation.
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From subjective evaluation of risk and application of policy and business rules to objective risk evaluation through leveraging data, use of scoring techniques, and consistent application of policy and business rules.
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From high origination costs to efficient processing of applications leading to faster time-to-decision and time-to-market, leading to lower costs.
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From legacy systems with slow development and adaptability capabilities to modern, agile, low-code systems with faster development and configuration culminating into fast deployment.
What's next for MSME Lending?
Here is how the credit scenario needs to evolve for MSMEs:
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Recognising the Sector - A change in the business model with dedicated processes and teams serving MSMEs. Banks must treat enterprises of different sizes with different data and credit needs differently.
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Customer Experience - Refined business models focusing on delivering a seamless experience across channels, speedy credit extension to customers, and reduced relational banking.
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Flexible Digital Tools - Adopting modern and agile loan processing and decision support systems that optimize the process, improve customer experience, ensure efficiency gains, and enable adaptability.
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Data Drives Everything - More structured data collection, automatic retrieval, and processing of data, building predictive models, leveraging internal and external data.
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Automation - Automating different aspects of the credit origination and monitoring processes.
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Challengers and Partnerships - Disruption by new entrants, including fintech companies, drives the business away from banks. Banks may explore collaboration and partnership opportunities with Fintechs instead of competing with each other.
Benefits for the banks to move beyond Loan Origination
With data-backed analysis and credit monitoring, banks can aim towards the following:
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Enabling credit decisions and loan covenants based on the risk appetite.
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Retaining a profitable customer base by upselling to profitable clients and reducing the exposure to risky clients.
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Managing regulatory and risk control with early warnings with Artificially Intelligent credit systems and periodical ECL calculations to set aside the impaired amounts.
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Optimising cash flows with focused recovery efforts of potentially delinquent portfolio.
How can CRIF help you in bridging the gap?
To spot the good borrowers from not-so-good borrowers, the banks need to set the inputs right to get the best output.
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Data Requirements
Banks need data concerning the owner, business, industry, economy, financial and credit repayment information, transactional data, and existing charges on collateral and securities.
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Leveraging Data
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Such data can be retrieved from internal sources, including existing transactions and loans, external sources like bureaus, corporate registries, etc.
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Such data, once sourced, can be validated with quality checks on all input fields and the creation of synthetic indicators.
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All derived variables can be calculated automatically with appropriate data visualisation.
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Automation is not only limited to an automatic application decision but automation of several credit processes, including credit appraisal, loan pricing, credit limit, loan disbursement, client communication, borrower notifications, and MIS/regulatory reporting.
CRIF has been a Special Accessing Entity (SAE) of Credit Information Corporation since 2016 in the Philippines. With data of more than 21 million individuals and 1 million businesses with 50 million credit lines, CRIF can be your ideal partner for data analytics, SME scoring, and rating system and implementing PFRS9 for your organisation.
Owing to its industry experience and availability of large customer data with several variables, CRIF can help you bridge the gap and build a robust credit framework. Our key services range from assisting in scorecard development, validation of data sampling, monitoring data processes, and implementing the process to automate credit risk grading.
Here are some of the specific areas wherein CRIF can assist you:
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Providing suitable inputs and data points for informed decision-making.
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Leveraging CRIF experience with similar projects in many financial institutions and banks in Europe and other countries, including detailed analysis of several different angles of your processes.
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Providing clear recommendations on the initiatives to be implemented to close gaps against the leading practice and best planning to optimise ROI.
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Implementation of the best-in-industry practices to assess and benchmark the credit risk assessment, leading to better asset quality, suitable risk pricing, and consequently better returns on investment.
To learn more about our solutions for your business, contact us to start a conversation.
