Challenges with Underwriting
Manual Underwriting can be a challenging task, with intricacies, nuances, and a high likelihood of human error. It’s no surprise that relying solely on manual procedures can consume a lot of time and resources for human underwriters, impacting both profitability and customer satisfaction. Some of the underwriting challenges include:
- Data Quality & Availability
- Risk Assessment
- Claims Fraud
- Regulatory Compliance
- Data Privacy
What’s required is a solution that can deliver enhanced precision, faster processing times, and reduced errors—all without necessitating an increase in your workforce or placing additional demands on your IT department by introducing another infrastructure rollout.
Automated Underwriting System for Quick Decision-Making
Underwriting automation in financial institutions can eliminate low-value manual efforts and enable faster decision-making for employees. Also, using an automated underwriting system to automate multiple tasks involved in the underwriting process. Let’s discuss what can be automated in insurance underwriting using automation and AI solutions.
To assess credit risk, underwriters have to collect data like policy documents and loan details from multiple sources and formats. According to McKinsey, “anywhere from 30 to 40 percent of underwriting’s time is spent on administrative tasks, such as re-keying data or manually executing analyses.” And getting all the data in one place is a time-consuming exercise for underwriters.
Here automation with intelligent document processing capability can handle this process and can do a lot better. Intelligent document processing in the underwriting process can easily extract data like loan and insurance documents and accumulate them in one system. From there, underwriters can check, analyze, and verify the data before making a decision.
As per a report, analysts spend up to 40% of their time validating data relevant to their analysis before making any conclusions. Using an underwriting automation system reduces the need for humans to collect data, underwriters get a full picture of data in one place. This enables underwriters to analyze large volumes of data in real time and assess the risk factors like credit score, underwriting documents, and customer behavior to make accurate decision-making. Moreover, machine learning algorithms can be trained to get historical data that can further help underwrites predict future risks and losses.
When there is a large volume of data to analyze and verify, human error is evident. With high-volume records, underwriters are often unable to detect fraud before it occurs. But that’s not the case with automation. Using machine learning and AI technologies with automation, underwriters can identify the inconsistency in the information provided and flag suspicious activities. This further reduces the chance of fraud in the business and saves the business from any financial losses.
Regulatory Compliance Management
The underwriting process must be in sync with the regulatory compliance offered by the financial institution. Any errors or discrepancies lead to financial risk and process inefficiency. Also, performing this process manually leaves the chances of manual error. Here, an automated underwriting system can be programmed to adhere to regulatory requirements and compliance standards and reduce the risk of non-compliance and associated penalties.
In the ever-evolving landscape of finance, embracing automation in underwriting not only brings operational efficiency but also ensures faster decision-making, improved accuracy, and, ultimately, enhanced customer satisfaction. As financial institutions continue to explore and implement these technologies, they are poised to reap the substantial benefits of automation in the world of underwriting.