A British financial technology company is investing in Bangladesh to collaborate with banks and non-banking financial institutions and revolutionise traditional lending systems in emerging markets using artificial intelligence (AI) and machine learning (ML).
Agam International -- a UK-registered firm -- has already signed agreements with four Bangladeshi banks to help them leverage AI and ML for credit decisions.
"We are now in talks with eight more banks, as several large conglomerates and industries are also keen to sign agreements with Agam in Bangladesh," Shabnam Nida Wazed, founder and CEO of Agam International, told The Financial Express through a virtual meeting from the UK.
She said Agam is working to shift the focus of the banking paradigm from collateral-based lending to character-based lending.
"Our intelligent system enables us to understand customers, helping them to become creditworthy for banks," she said.
Agam has already invested around Tk 70 million in about a year and aims to significantly increase this in the coming years.
"We are not a lender; we don't provide loans," Ms Wazed said. "We assist lending agencies or financial institutions in making informed real-world lending decisions using advanced predictive analytics."
"Our intelligent credit scoring engine learns from customer behavioural insights and reduces risk in real-time," she added.
The Agam CEO said their clients are primarily institutions for whom they analyse data and make recommendations.
The Agam system makes lending dynamic, upgrades conventional banking and allows onboarding customers with a single application, she said.
Agam's proprietary credit scoring engine is an AI-powered platform driven by a sophisticated LARC (Long-Acting Reversible Contraception) algorithm that predicts creditworthiness, including factors like potential for defaults, delinquencies and customers' ability and willingness to pay, while also considering their growth potential and eligibility for other products.
Agam's AI and ML models analyse demographic data, geolocation, behavioural responses, previous transaction records, psychometrics, KYC documents, financial literacy and access to resources to inform decisions for financial institutions.
She said Agam doesn't collect personal information as it respects individual rights. The data they analyse is collected by partner banks with customer consent and then shared for analysis.
Agam credit score is divided into stages, each with its perks and eligibility.
She said it's fully customisable based on the risk appetite and preferences of each bank, NBFI, MFI or insurance company.
The CEO added that client institutions can further tweak the algorithms to fit their specific loan products and customer bases.
Citing an example, Agam CEO said they signed an agreement with a private bank for pioneering early salary access -- a pilot called Earned Wage Access.
Under the system, the employees of the bank would be able to access their wages before payday, improving their financial stability and limiting the need to borrow loans through informal lenders, she said.
"Earned Wage Access not only allows instant access to funds but allows the ability to pay for unexpected expenses, empowering individuals to manage their income on their terms," she said.
Ms Wazed thinks that their algorithm-based application can help CMSMEs in Bangladesh get loans in the shortest possible time without any collateral.
"Digital financing emerges as the catalyst that not only addresses financial needs but also propels CMSMEs into a realm of increased efficiency, market reach, and global competitiveness," she said, adding that it would ensure sustained economic growth and financial empowerment.
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