Fintech has undergone an impressive transformation through the utilisation of advanced technology to improve financial services and streamline operations in the industry. Through automated financial services, improved security measures, and the integration of blockchain solutions, organisations are revolutionising their approach to delivering financial services to customers with the aid of AI-driven technologies.

As traditional lending systems often result in lengthy processing times, modern lending institutions are increasingly adopting automated loan approval systems to expedite the lending process. The benefits of speed, efficiency, and accuracy offered by automated systems are enjoyed by both borrowers and lenders. This blog aims to highlight the reasons why credit agencies should consider transitioning to automated loan approval systems.

In today’s market environment, retail businesses are continuously searching for cutting-edge solutions to establish a competitive advantage. One such solution that has garnered considerable attention is predictive analytics. By harnessing ML modeling and advanced tools like Corestrat’s Model.ai, retailers can unleash the complete potential of predictive analytics. Its applications span a wide range of areas, including demand forecasting, customer segmentation, pricing optimization, and fraud detection, among others.

The emergence of advanced loan origination and lending management systems, like Corestrat’s Digital Lending Automation platform, has introduced fresh opportunities for lenders to revolutionise their processes. Through leveraging automation, compliance, customization, and data analysis, lenders can access notable advantages, including heightened efficiency, simplified compliance, enhanced borrower satisfaction, and informed decision-making based on data.

Financial institutions are constantly striving to reduce delinquency rates and manage risk effectively in the dynamic world of lending. Predictive analytics and machine learning (ML) are revolutionising the lending industry, enabling lenders to improve profitability and optimise risk. Through the utilization of ML models, lenders can enhance the accuracy of credit scoring, prevent and detect fraud, optimise collection processes, predict customer lifetime value, and achieve a risk-optimized customer portfolio.

Alternative data has the potential to revolutionise credit risk management by offering companies more comprehensive insights into their customers’ financial behaviour and overall financial condition. Recent advancements in data sources and technologies allow businesses to capitalize on first-party data and develop ecosystem scores that are more effective, enhancing decision-making capabilities and driving business expansion.

Digital banks are no different than other banking institutions when it comes to risk management. With the threat of different risks looming large on digital banking sector, we, In this blog, will explore the various risk management strategies that digital banks must utilise to protect their customers and remain compliant.

Analysis of massive datasets manually can be time-consuming and inaccurate in today’s environment. As a result, data-driven decisions in the financial sector are increasingly being made using AI. In this blog we will look at the impact of predictive and prescriptive analytics in the financial sector.

Corestrat is pleased to announce that it has been recognized by the Singapore FinTech Association (SFA) as a certified FinTech. Corestrat has been awarded the SFA FinTech Certificate for its contribution to improving and automating the delivery of financial services to end users, businesses, and financial institutions.