1. Faster Loan Closures for Accelerated Growth
Using AI, OCR, NLP, and automation in digital lending, 90% faster loan closures are observed across the industry. This automated digital lending solution helps in mitigating risk with better credit decisions. In addition, automated loan origination optimizes onboarding processes by efficiently processing customer data and streamlining activities across cross-functional teams. Therefore, minimizing the manual efforts on data handling and saving the banks from juggling the volume of data.
No credit records? No worries. Digital Lending enables application processing while considering your guarantor or co-applicant details. You can raise loans quickly without a credit history. That's how lending is revolutionized across the BFSI space using digital capabilities.
2. Take Down First-party Frauds
With the high volume of data and loan applications flowing daily, a higher dependency on manual interventions and sub-optimal data management might expose banks to operational risks and external threats. While well-documented policies and regulatory guidelines exist, the steady rise in fraudulent activities requires tight risk monitoring. Hence, traditional ways of operations are not sustainable. Automated solutions and reducing overdependency on manual checks are the first step to staying ahead of first-party frauds.
3. Hyper-collaboration Between Teams
The gains from digital interventions are much more than investing in manual headcount. It enables non-linear scaling by minimizing frictions, reducing costs, and surging revenues. Furthermore, automated workflows and AI-enabled channels induce a modernized way of consistently processing information and its dissemination to cross-functional teams. This augments the decision-making process and holistic data management by giving the right teams access to the correct information at the right time, ironing out communication challenges, and promoting a collaborative working environment.
4. Amp up your Operations with Faster Decision Making and Analytics
Rising competition and customer lending alternatives put underwriters under much pressure to ensure efficient underwriting. Profitable and efficient underwriting operations demand high accuracy, credit decisions, and operational agility. Both of which are impossible to achieve without advanced technology. The concept of augmented underwriting helps banks and lending institutions in multiple ways. Banks can use machine learning models to improve their speed and accuracy of data processing. These machine learning models upgrade themselves as they analyze customer data and creditworthiness. With the help of automation in these decision processes, underwriters can free themselves from the extra pressure of the sub-processes like evaluation of risk scores, application scanning, and review, to name a few. Instead, the underwriters can focus on better decision-making with 100X more data points and tweak the rules and models to ensure a balanced human and machine intervention, thereby stepping up the underwriting operations. Powerful analytics enables a smoother outcome with faster and more accurate decisions.
5. Improved Customer Experience with Significant Retention
Customer delight and retention are equally crucial for a lender's growth. After all, it's the customers this industry is directly serving. CX will always be at the core of any new changes or transformation program. Customers are a vital pillar that cannot be ruled out while implementing any new process or technology. Digitalization not just sets things up internally but also ensures customers are engaged and well-informed of the additions and changes for a lasting relationship. Full transparency into the processes is the key, and the new digital channels ensure that it gets done in the best possible manner. Thus, a customer-centric digital-first approach is what businesses need to look at for an impactful customer experience.