Case study: What is bank transaction categorization?; Emerging Embedded Finance products: ERPs and payroll software workflows; Mastercard’s vision for Multi-Token Network;

In this edition, we're bringing you the latest insights on bank transaction categorization, the rise of real-time payments, and emerging embedded finance products.

Insights & Reports:

1️⃣ Case study: What is bank transaction categorization?

2️⃣ Real-time payments: efficient and fast

3️⃣ Emerging Embedded Finance Products: ERPs and Payroll Software Workflows

4️⃣ Mastercard’s vision for Multi-Token Network

5️⃣ The evolution of digital identity

6️⃣ Fintech Law Southeast Asia

Curated News:

1️⃣ JPMorgan Chase hires new chief technology officer

2️⃣ eBay Launches Venmo as a Payment Option

3️⃣ Apple discontinuing Apple Pay Later, ahead of new features launching this fall

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TL;DR:

Let's kick things off with a fascinating case study on bank transaction categorization, featuring Codat’s cutting-edge approach. Codat’s tech enhances banking data with accounting data to provide lenders with detailed insights into applicants’ transactions. This robust engine categorizes transactions using over 250 financial categories, predicting categories even when there’s no accounting data match.

Next up, real-time payments (RTP) are transforming global payments, especially in countries with government-led initiatives. With 70+ RTP networks worldwide, the demand for 24/7 money movement is being met, driven by both consumer demand and government efforts. This rapid adoption highlights the importance of coordination, often spearheaded by government mandates, to kickstart these networks.

We also explore emerging embedded finance products within ERP and payroll software workflows. From automating payments and issuing company cards to facilitating international transfers and offering business loans, embedded finance is revolutionizing financial workflows. The economic model involves revenue sharing and manageable ‘cost to play’ fees for SaaS providers, making it an attractive avenue for growth.

Mastercard’s vision for a Multi-Token Network is another game-changer. This network would enable highly regulated financial institutions to support digital assets securely across multiple blockchains, offering features like zero-liability guarantees and standardized smart contracts. This initiative could significantly boost public adoption of digital assets.

On the topic of digital identity, we trace the evolution from centralized identities with multiple passwords to federated identities and the future promise of self-sovereign identities (SSI). SSI offers greater control and security, making onboarding smoother for users and businesses alike.

In reports, we look at the fintech landscape in Southeast Asia. With regulatory frameworks improving, the region presents promising opportunities for fintech founders.

And now for some curated news: JPMorgan Chase has appointed Sri Shivananda, a former PayPal executive, as their new CTO. eBay is enhancing payment flexibility by adding Venmo as a payment option, catering to digital natives. Lastly, Apple is discontinuing its Apple Pay Later service to make way for new features this fall.

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Insights

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Case study: What is bank transaction categorization?

Bank Transaction Categorization

Codat’s approach

Here’s a closer look at the technology and how it works:

🔸 Step 1: Data is fed into the engine

Bank transaction categorization engine can operate on banking data alone, or lenders have the option to enhance it by adding accounting data. This allows for more comprehensive insight into the applicant’s transactions.

Lenders also have a choice of how the data is fed into the engine. Codat clients can utilize its accounting integrations and partnerships with Plaid and TrueLayer to enable applicants to link their bank account and accounting system (if required) with a few clicks using our connection flow. Alternatively, they can share transaction data they’ve sourced independently via our API or file upload.

🔸 Step 2: Transaction data is enriched with additional information

When both banking and accounting data is connected, Codat will search for a match for the bank transaction in the accounting data based on the date, amount, and counterparty associated with the transaction. Where a match is found, Codat then utilize the account type details associated with that transaction (e.g., income, expense, asset, liability, or equity) to offer lenders additional context.

In standard bookkeeping procedures, account transactions are recorded against one of the nominal accounts that appear on the Profit and Loss (Income Statement) or Balance Sheet. Codat uses this information to categorize the nominal account under one of our 250+ financial categories. This ensures more accurate categorization based on the actual accounting behavior of businesses, rather than consumers, which is common with other providers.

🔸 Step 3: Details for unmatched transactions are extracted