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Debt Collection In Malaysia: An Overview




According to the Department of the Statistics (DOSM), while Malaysia’s key economic indicators for the first five months of 2021 showed encouraging performance, the Leading Index (LI), which anticipates the economic direction in the near future, posted a slower growth of 6.9% in May 2021 compared to 15.7% in April 2021.


With the ongoing Covid-19 pandemic still at large, the cost of living continues to rise, while many Malaysians in the workforce are being laid off or getting a massive pay cut. As such, plenty of businesses alike are finding it more difficult to deal with debt collection and management.


One of the issues faced by financial institutions and moneylenders includes customers not paying the full amount of their monthly instalments as agreed upon. Or worse, defaulting on them entirely and filing for bankruptcy. However, it isn’t just Malaysian households that are defaulting on debts but corporations too.


Avoiding non-performing loans (NPLs)


This forces moneylenders to lower the instalment amount to avoid the loans from becoming a non-performing loan (NPL). NPLs are defined as loans that have been overdue for more than 90 days. Based on the recent CEIC data, Malaysia’s non-performing loans ratio stood at 1.6 % in June 2021, compared with the ratio of 1.6 % in the previous month.


How are NPLs calculated? The non-performing loans to loans ratio are calculated by adding 90+ day late loans (and still accruing) to nonaccrual loans and then dividing that total by the total amount of loans in the portfolio.


NPL is a problem that has been plaguing the country’s money-lending industry for decades. According to The Edge Markets, the last time the country experienced poor debt collection, which eventually led to a serious non-performing-loan problem, was during the 1997/98 Asian financial crisis.


That said, the circumstances today are vastly different from that period. At the time, banks would overextend credit and the distress was mainly in corporate lending. Meanwhile today, the industry is dealing with a high level of indebtedness among households.


Disposing of NPLs as part of risk management practice


According to Bank Negara Malaysia, banking institutions can dispose of their NPLs as part of the bank's risk management practice. Disposal of NPLs provides the flexibility for banks to manage their loan portfolio effectively and efficiently to maximise recovery. Of course, any recovery action must be in accordance with the law.


Banking institutions are permitted to sell their NPLs to non-banking institutions provided that the sale of NPLs is made in accordance with the requirements of the Guidelines on the Disposal/Purchase of Non-Performing Loans by Banking Institutions which are issued under the Banking and Financial Institutions Act 1989.


Among the requirements set out by the Guidelines that must be met by any banking institution proposing to sell NPLs include the following:


  1. Banks can only sell to locally incorporated companies, which the purchaser is majority owned by domestic shareholders as the purchaser is subject to a foreign equity cap of 49%.

  2. Banks are also required to undertake necessary measures to inform the borrower of the sale of the NPLs;

  3. Sale of NPLs that is made in accordance with requirements of the Guidelines should not contravene the BAFIA.

  4. Sale of NPL does not affect any debt restructuring agreements.


Fintechs specialising in debt collection software could be the right solution to avoid NPLs in the current situation


It is no longer enough for business owners to depend on cost-cutting, old technology, ineffective and inefficient collection processes, and rigid collection strategies for debt collection.


A few Malaysian fintech now specialise in debt collection software offering a seamless debt collection experience for all.


Such technology helps to implement different collection strategies for different customers to achieve the most efficient use of resources. This is done via machine learning algorithms embedded within the system.


This helps one to identify potential non-performing loans with up to 96% accuracy. This way, banks, financial institutions and enterprises can easily identify the collectability of their customers/debtors.


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