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Data is the new oil, yes. But how does that work? Financial Services Providers (FSPs) possess large amounts of customer account information (customer transactions) which have the standard characteristics of big data; high in volume, variety and velocity. This presents a challenge in generating insights from the data. The human brain is wired to be able to find patterns in noisy data, which is why historically FSPs have had to hire quant specialists to process the ever-growing number of bank statements. However, with transaction categorization technology this data can be processed automatically using transaction algorithms. 

Algorithms make it possible to screen bits of information in a transaction’s description or information field and categorise the transactions according to specific pre-set categories. Further categorization allows transactions to be grouped according to “themes” which provide insights for product and lending decision making.  These insights show patterns in a large amount of available transaction data in real time. To help, FSPs understand we have compiled a list of value-adding use-cases FSPs can leverage transaction categorisation technology to create enterprise value.

9 use-cases for account information

Once you can categorise transactions, the opportunities to use the new clean data are almost limitless. Here are some of the most useful applications for categorised account data and analytics.

  1. Verify real income — Often a loan applicant has multiple income streams, including freelance work or perhaps side jobs, such as driving for Uber. Account data categorization allows you to identify the salary and other regular or irregular income for all new or existing customers.
    • Value: generate the missing data required to get the full information about customer’s income for lending decisioning.
  2. Identify existing low-risk customers (for retail banks) — For retail banks to increase the conversion rates on marketing campaigns, it’s a great idea to know the segment of the bank’s customers that are likely to be approved for a loan. In cases where the bank already has a large number of accounts and cardholders, account data allows FSPs to calculate the probability of default for existing customers using only historic transaction data and historical loan performance.
    • Value: identify low-risk customers that were previously underserved.
  3. Create new customer profiles (for retail banks) — Customer segmentation is a great way to target specific audiences with the right message. Transaction categorisation can provide unique new characteristics to profile customers for campaigns (based on where and how they earn/spend their money i.e. customer spendography), including identifying tech-savvy individuals, prime borrowers, to mention a few.
    • Value: build more tailored offers and marketing campaigns that deliver higher success rates.
  1. Verify active loans — Credit bureaus might not always provide the full picture of all liabilities a person has.  They are sometimes hampered by lenders’ inability to regularly update loan reports and loans not covered in credit registry (loans from the shadow lending sector). Account data allows FSPs to identify all incoming and outgoing loan payments, including loans that are not registered in credit bureau agencies.
    • Value: better insights on actual liabilities and can provide the missing data required to score or pre-score customers.
  2. Discover risk-reducing behaviour — In lending, the context of the loan applicant is important and in many cases, swings a decision balance. Account data allows FSPs to capture insights that give context to customers who have been previously defined as “high risk”. It can illuminate reasons for previously overdue loan payments or transfers on behalf of family members.
    • Value: reduce wrong decision-making due to potentially misleading/inadequate information from credit bureaus.
  1. Check “red flags” — Credit history and income gives a good first impression of a loan applicant, however, to get the full picture of the applicant’s financial health, it’s a good idea to look for additional signals. Account transaction data allows FSPs to identify behaviour patterns like excessive gambling, frequent cash withdrawals or concealed bailiff and debt collection cases, among other possible behaviours.
    • Value: more insight to automatically identify high-risk customers and improve decision making efficiency.
  1. Identify unusual behaviour — Recognising potential fraud cases is crucial to maintain a healthy loan portfolio. Account transaction data allows FSPs to spot potentially fraudulent behaviour, including frequent payments to accounts in other financial institutions (i.e. secret accounts), salary payments to or from other individuals (i.e. false salary payments), “1-cent” verification transactions to other lenders (i.e. unmentioned liabilities).
    • Value: more insights to automatically identify high-risk customers and improve decision making efficiency.
  2. Discover new features for credit scoring models — Building new scorecards requires a trustworthy data source with behaviours (or features) that can be predictive when assessing a loan applicant’s ability to repay the loan. Modelling features generated from transaction data capture effects that are not captured by traditional credit data.
    • Value: more accurate predictive scoring or pre-scoring models.
  3. Support customers with regular payments (for retail banks) — When on-boarding new customers, it’s important to make them feel at home from day one. Knowing what payments a customer makes regularly allows FSPs to build solutions that can help a customer make these payments faster and improve the overall customer experience.
    • Value: improve overall customer experience across banking and payment touch points.

Thanks to the development of the open banking initiative, it has become possible to considerably increase the value generated from the collection of customer transaction data. Listed above are just a few of the most immediate ways financial institutions can make use of and capitalise on categorised account data. If you’re interested in diving deeper into transaction categorisation, get in touch with us. We’ll be happy to offer our input and support you as you discover how to make the most of your collected customer account information.

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