Why Should You Care About Banking with Artificial Intelligence (AI)


As the banking industry moves towards a parallel between Islamic and Conventional banking, we witness the increasing uptake of Islamic Banking products as financial institutions promote its benefits, and as digitalization of financial services enable more unbanked community to be reached.

Islamic Banking can prove to be a tedious business, as financial institutions need to ensure that Islamic agreements (Aqad) adhere to the Shariah law. As such, there are higher costs in offering Islamic Banking products in terms of system configuration, compliance, and customer service.

Banks are also required to appoint Islamic product auditors and train their staff to ensure proper compliance and validity of the processes. These add to the cost to Islamic banking providers. This article illustrates how Artificial Intelligence (AI) can help optimize and manage the cost of providing banking services and products.

Application of AI in INFOPRO’s Banking Solutions

Embedding Artificial Intelligence (AI) into systems will reduce most of the cost in the processes. A study estimates that the financial services industry will save $1 trillion by 2023 by incorporating AI into their operations (Nida, 2020).

Conventional and Islamic Banking solutions have been INFOPRO’s key product library since our inception in 1987. Continuous R&D and investments in the latest technologies have ensured that our banking solutions remain relevant and contribute to a higher value and profitability for our clients.

Application of AI in INFOPRO’s Banking Solutions


Credit Scoring

It is predicted that $490 billion will be saved by 2030 if AI is incorporated in front office tasks (Joyce, 2018). A key task in onboarding a new loan customer is conducting credit scoring assessments. However, formulating an accurate credit scoring engine is time-consuming and tedious.

Accurate credit scoring is especially required to onboard and bring Conventional and Islamic Banking products to customers with limited credit information, such as the unbanked community. INFOPRO’s AI-based credit scoring system leverages on clustering technology to find relevant credit patterns among the underserved segments. With INFOPRO’s AI-powered credit scoring system, more efficient and accurate credit scoring can be conducted to evaluate new loan applicants, and subsequently, increase financial inclusion.

RPA (Robotic Process Automation)

RPA technology embedded within INFOPRO’s Digital Loans Origination System reduces the occurrence of errors in data input by automatically extracting information from documents using the power of Natural Language Processing (NLP). The system is able to conduct auto-approvals once specific thresholds are met, and risk levels calculated. Ultimately, customers can be onboarded more seamlessly and quickly, and at a lower cost.


AI in AML/CTF processes can help financial institutions combat crime. AI engines power the AML solutions to ensure that true positive transactions are captured and flagged while monitoring all other risks throughout the process. Having an intelligent AML/CTF solution helps financial institutions reduce the risk of non-compliance while increasing the efficiency of monitoring.

Middle Office

Customer Segmentation

Traditional customer segmentation relies on demographic, geographic, or other readily identifiable patterns. On the other hand, customer segmentation that has been augmented with AI can unlock other aspects and patterns that lead to more accurate and relevant customer groups. With this information, financial institutions can enhance areas of marketing, product development, churn analysis, and many more.

Product Development

Riding off more representative customer segmentation, financial institutions will be better equipped to develop appealing and relevant products to target AI-specified customer groups.

Back Office

Risk Management

INFOPRO provides full risk coverage with our Enterprise Risk Management solution, which uses various machine learning techniques to build risk management models. Risk models built can be used at the overall risk level or granular product levels, such as housing loans, automotive loans, and more. Some models are even capable of conducting back-testing and validation to enhance transparency.

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