A lack of reliable data on the latest consumer profiles has caused inefficiencies in the credit decision-making ecosystem from a traditional credit data report standpoint. This has been evinced by various moratoriums and payment holidays.

It has therefore shaped a need to have more alternative, real-time, updated sources of data to shed more light – and complete a more representative and holistic view – on the latest and updated risk profiles of consumers and businesses.

The inherent information asymmetry on borrowers, especially the underbanked segment, makes it challenging for lenders to provide credit without facing high or unknown risks.

Experian, the world’s leading global information services company operating in 45 countries, including Indonesia, recently launched Experian Boost in the US and UK, which seeks to help lenders get a more complete view of a consumer’s creditworthiness. 

It is a first-of-its-kind financial tool that empowers consumers to add positive utility, telecom bills and Netflix payments directly into their credit file to improve their credit scores in real time.

To date, over 35 million total points have been boosted, leading to a credit score increase for more than 2.5 million consumers in the US. This is why new data sources can have such a key role across all types of credit access options available in Indonesia today.

Maximising loan approval rates

Online shopping is popular among Indonesian consumers even before the COVID-19 pandemic, with more consumers nudged onto online marketplaces. About 90 percent of Indonesia’s 152 million internet users have transacted online before.

To make it even easier for shoppers to buy online, the country’s top e-commerce sites offer “buy-now, pay-later” loan programs, which have become the third most popular fintech product in Southeast Asia’s largest economy. This has removed barriers to online shopping for Indonesia’s estimated 273.5 million people.

Experian’s alternative scoring solution combines “ready-to-use” alternative data with adaptive learning to generate an alternative score even for Indonesian e-shoppers who do not have a bank account or credit card. It is tailored specifically to the needs of the industry and the profile of target customers. Alternative scoring has also been implemented with a leading digital credit card provider in Indonesia.

The solution has enabled a wider base of applicants, including those from the unbanked segment, to get access to a first credit card. This allows the provider to grow their credit portfolio, bringing financial inclusion to hundreds of thousands, while maximizing the loan approval rates and keeping the rate of bad debt constant.

Meanwhile, with the objective to bridge the loan gap to middle-income consumers, a P2P lending platform can now offer quicker, short-term loans (6 to 12 months) of smaller amounts ranging from US$120 to US$1,400 through the firm’s smartphone application or website. This is supported by the deployment of alternative scoring means of additional insights for such credit decisions.

Like in the United States, where many consumers are willing to provide additional information to lenders for improved credit access and nearly 90 percent of lenders believe that alternative credit data allows them to extend credit to more consumers, Indonesian lenders are becoming more open to using alternative data that leverages non-financial information, such as telecommunications data, for identifying the credit invisible and as a complement to their existing risk assessment scores to support better credit decision-making, according to Mohan Jayaraman, Managing Director for Southeast Asia and Regional Innovation at Experian APAC.

“We are witnessing a positive shift in organizations providing alternative data for risk assessment. In Indonesia, we work with clients across banking, multi-finance and fintech, and our clients are increasingly embracing new sources of data to better inform their credit decisioning, allowing them to approve applicants that previously would not be approved due to a lack of data,” Mohan said.

“When it comes to risk assessment, we combine the power and speed of insights derived from Machine Learning with our 25 plus years of domain expertise to ensure that the resulting models are highly predictive as well as explainable to clients, regulators and consumers.  This explainability is vital,” Mohan emphasised.

By partnering with Experian, which offers the aforementioned strengths and value propositions, you as a lender will be able to answer the challenges arising from the lack of real-time and reliable data sources in a correct manner.

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