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Tailored Solutions for Enhanced Decision-Making

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Bank Statement Transaction Labelling and Analysis

PSD2 has enabled any company to ask for bank statement data during customer onboarding. But this data is useless unless you have machine learning models that can label each transaction accurately and calculate necessary variables.

Problem

Understanding transaction data requires knowing what each transaction means, this is difficult if you are just starting out.

Most labelling solutions have not been used in real machine learning models for decision making. RikBite model has been used by 10’s of FinTech players in Poland for most critical decisions.

Solution

RiskBite has developed a model that labels each banks statement record through supervised machine learning methods.

This means RiskBite experts have reviewed thousands of bank statements and on a sampling basis does for review purposes. These labels now works as benchmarks for training the machine learning model.

Each country is different, and RiskBite’s solution is the highest quality bank statement algorithm in Poland.

Technology

  • Machine learning
  • Data-set of >500k banks statements in Poland
  • Accurate calculations about income and expenses
  • AIS gate-away access via partner offering (in Poland)
  • API access

Use-case: Unsecured B2B loans – Western Europe

  • Transaction Labeling and Analysis Mechanism – Constructing systems to classify and scrutinize transaction data, enabling more informed lending decisions.
  • Dedicated Scoring Models for Customers – Creating bespoke scoring models that assess the creditworthiness of new and returning customers, enhancing risk assessment accuracy.
  • Optimal Loan Terms Design – Designing and implementing algorithms to select optimal loan amounts and repayment dates, based on an analysis of the borrower’s income history.
  • Decision Engine Provision and Maintenance – Providing a custom-built decision engine and ensuring its ongoing effectiveness and reliability for loan processing.
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