FlexRule for Finance

Adapt to Change, Improve the Quality of Complex
Decisions, and Speed up your Time-to-Market.

FlexRule for Finance

Adapt to Change, Improve the Quality of Complex Decisions, and Speed up your Time-to-Market.

FlexRule for Finance

Adapt to Change, Improve the Quality of Complex Decisions, and Speed up your Time-to-Market.

In the past we needed three months to introduce new products, but after implementing FlexRule we shortened this to couple of weeks….It will only be profitable for our company if the whole process is automated. The FlexRule engine will cover most of the sales and risk decisions in our leasing process.
Artur Lipski
IT Leader, mLeasing

Orchestrate, Automate, and Manage
Complex, Long-running Decisions

Product Eligibility

Assess product eligibility of borrowers for specific product requirements based on multiple criteria, including their financial strength and credit history involving tens of thousands of decisions.
Advise borrowers on loan and credit options to satisfy their very individual needs. Ensure accuracy and consistency of the offers are maintained across all processes, applications, and channels.

Loan Origination Process

Map out the entire loan origination process including thorough due diligence of borrowers, underwriting and a complete risk assessment scoring, loan approval, and loan creation and disbursement and ensure straight-through processing. Make more objective, traceable, and transparent decisions and processes.

Loan Lifecycle Management

Automate and manage decisions regarding questions and forms as well as and the priority of activities and tasks across the entire loan lifecycle right from converting leads to applications to loan origination process to managing post-closing application activities.

Credit Scoring

Build credit scoring models, deploy and run these score cards against different scenarios to decide on the accurate and appropriate customer scores and product offers. Fully assess credit risk for decisions made across entire credit lifecycle considering a cross-portfolio view and different scenarios.

Credit and Lending Decision

Combine customer information and multiple sources of data; model credit, lending, and risk decisions; and determine the creditworthiness of an applicant based on calculations, formula, and business rules.

Data Quality and Validation

Data quality and validation can be defined and executed based on different business requirements and use cases across credit, risk, and lending process. Assess and validate if a customer’s personal data, earnings, and expenses fall within the acceptable range. Ensure the provided documents and information are valid and sufficient.

Pricing and Rating

Determine an accurate and personalized price covering the administrative cost of loan, credit score, repayment risk score, risks of a particular product type using the combination of machine learning-powered and rule-based aspects to build pricing models with maximum accuracy and transparency.

Debt Collection – Calculation

Build a decision model for complex debt-to-income calculation involving multiple steps, as a single source of truth across multiple processes and systems. Also, ensure compliance with regulations and rules are simple, consistent, and transparent.

Debt Collection – Effective Messaging

Identify the right messages to be communicated to the right agents through different channels such as SMS, Letter, Email, etc., across different stages of debt collection process. Review and change rules, and experiment with messages using A/B testing to build different scenarios across the process.

Fraud and Money Laundering Prevention

Detect and prevent fraud by analyzing large amounts of data across multiple IT systems and make situation-aware decisions. Combine predictive (data-driven decisions using AI/ML) and prescriptive (rule-driven decision) analytics to identify and prevent fraudulent transactions. Also, build robots that automate decisions using decision robotics.

Case Study

mLeasing, a leading European leasing company delivering even complicated products with frequently changing multiple parameters, much faster.

Case Study

mLeasing, a leading European leasing company delivering even complicated products with frequently changing multiple parameters, much faster.

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