Innovation, market competition, and more compelling services for end users are integral elements of open banking foundations. They mostly rely on the availability of financial data which is usually gathered from various sources, coming in many shapes and sizes. A great advantage for a creditor company is the ability to categorize transactions into income and expense categories and fully automate the credit decisioning processes.
Credit decisioning uses algorithms and data analytics to access a credit applicant’s creditworthiness quickly and efficiently. Automated decisioning engines, offered by the lending industry leaders, make evaluation and information-collecting processes much easier and allow them to make data-driven, informed lending decisions.
Categorization engine and credit-risk decisioning engine – simple, yet advanced solutions for your lending business
Open banking has paved the way for better financial products and services. They are tailored to the specific needs of customers, mainly thanks to the massive amounts of data used by financial companies to drive innovation. Modern banking solutions allow lenders to:
- Aggregate and analyze large amounts of data from numerous sources, providing a comprehensive view of an applicant’s financial profile.
- Make data-driven decisions quickly thanks to accessing real-time information.
- Manage risk properly.
- Utilize Machine Learning technologies and algorithms to improve decision accuracy over time.
- Better understand customer needs and provide customers with a more personalized customer experience. Companies can easily collect more information about their clients’ finances and habits which allows them to offer better tailored services.
- Thanks to payment APIs, open banking solutions offer higher conversion rates with seamless, mobile-first customer experiences. Also, lending companies can lower fees as open banking takes cost out of the equation for merchants.
In the world of a rapidly changing economy, companies need efficiency more than ever before. They develop solutions that make credit decisions accurate and cost-effective, very often using automated credit decisioning engines and transaction categorization processes. Transaction categorization engine’s accuracy allows lenders to validate major criteria specific to the credit decision and its automation. Those are:
- Creditworthiness (you can identify incompressible income and expenses among all banking transactions).
- Identification of red flags to avoid situations of over-indebtedness.
- Estimation of the probability of an applicant repaying their credit installments.
Modern automated credit decisioning engine solutions offer outstanding precision that nearly eliminates human error, impressive speed that reduces decision-making time, as well as successful risk mitigation. The engine is trained on millions of data points using ML algorithms and it constantly improves to recognize new payment types, merchants, and consumer trends.
One simple platform to manage automated credit decision-making
Being armed with a tool capable of making the most out of utilizing bank transactions may be a game changer in a rapidly changing world of lending. Planky, a powerful platform for handling financial data, is one of the best tools to both improve customer experience and boost engagement while providing services of excellent quality. It enables companies to access bank account data in an easy, automated, and fully regulated way so that you can quickly and efficiently verify your customers and suppliers.
Planky enables you to:
- Check the finances of your customers to instantly understand their spending habits and use this information to make smarter decisions.
- Gather all banking data in one place to monitor transactions in real time.
- Make things simpler and much more convenient – you don’t have to download documents from various sources.
- Bring your reporting processes to a brand-new level – data is gathered, processed, and ready to present in an easy-to-follow dashboard.
Unlike most automated credit decision platforms, Planky isn’t complex or hard to follow. Thanks to a user-friendly interface you can quickly and easily understand where certain decisions have come from.
Ready to make quick decisions that are 100% error-free? Start your free trial to streamline your credit decision-making process.
How do you know that a transaction categorization tool is successful?
Using a transaction categorization engine is a way to improve how a company uses financial data and presents it to its customers. The entire process requires building the right training set for the algorithm and setting proper categories to make sure transactions are grouped together as they should be. The more data you can provide, the faster your algorithm learns, and the more accurate categorization you can offer.
Conclusion
Transaction categorization engine and credit-risk decisioning engine are crucial parts of open banking solutions such as Planky. They help lenders build a more customer-oriented lending ecosystem. Such platforms shape the future of finance using AI, Machine Learning, and big data analytics. Their predictive capabilities and accuracy evolve which is an excellent opportunity for the lending industry leaders to improve their services and enhance personalization even more.
