There are three important components of the credit review process where technology plays a critical role in making the process faster and more efficient. Businesses gain an edge by leveraging technology to automate tasks involved in these components and save time and resources for more insight-driven, strategically important functions in credit management.

Credit Data and Decision Inputs

The credit management department fulfills its primary objective of business risk diminishment through credit investigations and audits. This process requires detailed data points from multiple sources that facilitate sound credit dissection and insightful credit reviews. The two types of credit data involved are:

• External Data ? Credit analysts spend a significant amount of time gathering backup data from credit reports from rating agencies, credit bureaus and trade groups, public financials, income statements, balance sheets, key financial ratios, and insurance details. This might involve web searches, telephone calls to references, and even faxes or emails from disparate sources. Such processes can be very time- consuming, prone to error, and subject to wide variations in how the credit policy is applied. Businesses could plug this time and productivity drain with the help of technology by automating data aggregation and gaining real-time access to relevant customer information. It provides a one-stop-shop of research-ready information to the credit analyst, thereby saving time for insightful analysis instead of the low-value tasks of data scavenging and compilation. With access to critical data in real-time, it is much easier to track exposure to risk, accrued provisions, and cash flow, not just on a daily basis, but hourly, be informed if a customer?s financial situation deteriorates, and put in place measures to limit the financial impact.
• Internal Data†- Customer-specific information available from internal business teams such as past orders and payment behavior forms an important component of credit score evaluation and credit reviews. With an automated credit risk management system in place, the analysts gain access to all of the relevant internal data in a standardized format and in one place. It saves them the time lost in cherry-picking information from multiple systems and sources and allows them to focus on the research and analysis functions of credit review.

Stakeholder Collaboration

Credit reviews require frequent collaboration with both finance and non-finance stakeholders for credit information and to gain approval and management sign-offs on credit decisions and recommendations. This process involves the exchange of sensitive information back and forth between departments and is thus prone to potential breakdowns due to correspondence issues such as lost documents, missing information, missed emails and phone calls, and misinterpreted details. This results in undue delays and lapses. Credit management automation technology provides users with workflows that eliminate the time-consuming and distracting work of manual collaboration with business teams. The workflow manages the process flow of stakeholder collaboration, notifies them of the open action items, and provides all of the supporting documents required to process the request. Nothing kills morale in a credit team like constant re-work. Hierarchical workflows enable credit analysts to obtain sign-offs from the right authority at the right time based on the dollar value or impact of the credit decision, thereby eliminating the time lost in re-initiating the approval process.

Credit Decisioning

It cannot be stressed enough how important the accuracy and quality of credit decisions are for determining optimum risk exposure levels and transitioning from a back-office function to a strategic driver of business growth. Companies stuck with inefficient and outdated methods for assessing the risk of extending credit to a customer are unable to transcend the quagmire of low-value, transactional tasks involved in the process. In the absence of a standardized system, credit policies vary across the organization and credit teams lose a majority of their productivity to re-work and processing ad-hoc requests. Consequently, little focus is given to delivering data-driven, insightful credit decisions. Teams struggle to meet their routine targets for processing new credit applications and conducting periodic reviews, let alone assume a more strategic, business growth-oriented role in the scheme of things. Automation of credit risk management enables the use of consistent decision frameworks that can be scaled and customized as needed. Most relevant internal or external data sources can be brought into the decision-making process, with decision frameworks that encompass your parameters to generate credit decisions that are consistent across all channels and drive maximum results for your business. Automation of credit decision frameworks and analytics also enables real-time monitoring for fraud and complete auditing for compliance purposes.