1. Overview

Before diving into the details of the data structure, it’s important to ground ourselves in the principles that shape what we collect, how we structure it, and why.

The design of our data structure is not just about what we collect — it’s about making sure every data point unlocks real impact. It must be relevant to the use cases we want to support, scalable across different institutions, feasible to implement based on existing data sources, and aligned with global standards from markets ahead of us.

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As the visual on the left shows, we’re not just digitising traditional statements — we’re transitioning from static records to real-time, usable data. This shift brings new possibilities, but also new responsibilities.

We embed key guiding principles:

• Customer control and accessibility

• Security and convenience

• Clear value for industry

• Broad ecosystem reach

• Ubiquity in touchpoints

This brings us to the bigger question we ask when designing the data structure:

Does this dataset help deliver better decisions, better products, and better outcomes — for both my customers and my organization?

2. Grounding in Use Cases, Not Just Data Volume

This section reinforces our north star: we don’t want a data-heavy framework — we want a purpose-built one. The opportunity lies in opening up structured, usable, and consented access to data that helps people:

• Understand their finances

• Get fairer financial products

• Build long-term resilience

And for institutions, it powers more accurate products, faster time to market, and lower risk profiles.

Every data point we request maps back to actual product needs — like real-time lending, inclusive underwriting, and tools that support long-term financial wellbeing.

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Let’s walk through two use cases next -

1. Personal Finance Management (PFM)

2. Credit underwriting

3. Use Case 1: Personal Finance Management (PFM)

The first use case addresses a simple but widespread problem: people don’t always know where their money goes. PFM tools powered by Open Finance aggregate balances, accounts, and transactions into one smart view. This helps customers make better day-to-day financial decisions.

For institutions, the benefits are real:

• Stronger customer loyalty

• Better repayment patterns

• Smarter targeting based on financial behavior

And it’s not just about showing balances. It’s about showing how and when people earn, spend, and repay — giving a deeper view of their financial reality.

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3.1 From Insight to Action: Building Financial Habits

Once you have that visibility, you can drive behavior change. The second visual shows how PFM features support both cashflow management and financial commitments -

As a user, I want to:

• Track my income inflows

• Understand where my money goes — debt, lifestyle, savings

• Monitor all my financial commitments — credit cards, loans, recurring bills

• Spot patterns — rising expenses, increased credit usage, tight cashflow

• See my actual repayment behavior — especially for loans

• Get reminders when payments are due, so I avoid late fees

• Receive smart nudges, including saving suggestions when relevant

 

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The goal is to build loyalty through stickiness — using reminders, alerts, and dashboards that turn insight into action, and help users build healthier financial habits.

Let’s look at what data powers this.

3.2 What Data Makes PFM Work?

Two key data sets are fundamental in Open Finance PFM which are

Why Income and Obligation Data are Vital in PFM

In today’s economic landscape, structured access to both income and obligation (loan) data is no longer optional, it’s essential. For Malaysians, the rising debt levels, uneven credit access, and widening income gaps all point to one thing: the need for better financial visibility and smarter tools to support both individuals and institutions.

The Table 1 below highlights key national statistics that demonstrate how income and obligation data are critical in shaping financial literacy, credit access, and economic sustainability in Malaysia, while Table 2 below outlines the amount, approval rates, and repayment patterns across major consumer loan types in Malaysia:

From the total RM 43.09 billion in retail loans disbursed, nearly 90% came from just three key loan types: credit cards, residential property, and passenger vehicles. These categories represent the most significant segments of Malaysia’s consumer credit landscape, both in terms of volume and economic relevance. As such, the Open Finance initiative will focus on these three loan types to maximize impact and address the real financial needs of consumers. Loan categories with lower demand or lesser systemic importance such as personal loans and securities will be deprioritized in the initial rollout.

By combining structured access to income data (what individuals earn and how stable it is) with obligation data (how much they owe and how they manage repayments), Open Finance enables a more complete and accurate picture of financial health. This empowers financial institutions to assess risk more responsibly, equips consumers with the tools to improve credit behavior, and supports policymakers in crafting more inclusive financial strategies. In doing so, Open Finance lays the groundwork for a fairer, more resilient, and better-informed financial ecosystem in Malaysia.

Breakdown of Data Required for Income and Obligation Information

i. Income & Inflow Data

These data points show how much money is coming in, from what sources, and how frequently. This enables the system to build a dynamic view of a user’s earning patterns, stability, and financial capacity. Enables accurate budgeting, income categorization, salary recognition, and financial health scoring.

ii. Obligations & Outflow Data

These reflect a user's repayment duties, behavior, and financial resilience. Understanding outflows allows the system to assess net disposable income, forecast risks, and issue early nudges before financial stress occurs. Supports cash flow planning, proactive debt management, and nudges to avoid missed repayments or over-leverage.

4. Use Case 2: Credit Underwriting

The second use case solves a different pain point: loan applications are often manual, inefficient, and inaccessible for people without formal payslips or credit history.

By using Open Finance data, institutions can pull real-time information on balances and transactions directly from source — giving lenders a fuller, more accurate view of financial health.

This unlocks:

• More inclusive risk models for gig workers and MSMEs

• Better credit decisions

• Faster processing with fewer documents

And for users: no more uploading pay slips, no guessing what they qualify for — just fairer, faster access.

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4.1 Better Lending Experience

From a user perspective, Open Finance-powered lending means:

• Skipping paperwork

• Getting matched to the right loan

• Being evaluated not just on credit score, but on behavior: on-time payments, early settlements, and responsible usage

The visual shows how this can be paired with product experiences like:

• Instant approvals

• 1-click loan applications

• Dashboards for large purchase planning

• Nudges to consolidate or refinance loans

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4.2 What Data Powers Smarter Lending?

The data powering this experience is remarkable like PFM but with additional emphasis on account verification and deeper credit detail.

Let’s now walk through the added value of Open Finance Data.

Value-Added Data in PFM & Open Finance

Your PFM model provides a richer, more current view of users' financial status through Open Finance. Here’s how PFM + Open Finance unlocks smarter, faster lending, even with a lean data scope:

This gives lenders the confidence to approve loans quickly, fairly, and with less manual input, while keeping the customer in control.

Together, these use cases show how Open Finance can transform financial experiences, not by asking for everything, but by asking for the right data.

5. In Summary

In summary, to enable these two critical use cases for Malaysia, the following key principles will define the data structure.

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Let’s now walk through the actual structure we’ve proposed, and how each data field connects back to the use cases outlined in Section 8.2: Data Structure, which is separated into Account Information Data, Balance Information Data, and Transaction Data