Operational Business Intelligence

InetSoft focuses on operational BI through an intuitive self-service user interface that facilitates the dissemination of information, and institutes accurate and timely decision making.

Requirements as diverse as reporting, OLAP, scorecards, dashboards, visualization, and ad hoc analysis are all met in a unified application that uses live key performance indicators to continually guide and gauge all business processes.

With cloud-based InetSoft's operational BI platform, businesses can:

  • Explore data with self service solutions - The visualization driven approach of InetSoft allows users to perform their own reporting and analysis with very little IT support. By using familiar graphical objects and an intuitive Web 2.0 interface end users are empowered to easily explore, manipulate, and display data to gain new insights.
#1 Ranking: Read how InetSoft was rated #1 for user adoption in G2's user survey-based index Read More

  • Connect easily to existing data sources - With its Data Block technology, InetSoft simplifies the process of accessing data. Users can retrieve information from data warehouses, transactional databases, or other sources regardless of whether they are stored as relational databases, XML, flat files, etc. This data can then be dynamically mashed-up, manipulated, and transformed as Lego like data assets.
  • Meet ROI goals - InetSoft has flexible pricing models that allow you to deploy a solution that accommodates your unique situation. InetSoft's architecture provides a total BI solution within one unified application.  However, if your needs dictate, deploy just the components that you need.
  • Scale to meet the needs of the organization - Built upon open J2EE standards our application provides a solution that incorporates clustering, load balancing, and failover to provide high availability and quick response time. Additional features such as page streaming, data caching, and performance tuning ensure optimal operation as your needs increase.

  • What Is Operational Business Intelligence?

    Operational business intelligence is defined as real-time business intelligence derived from ongoing business operations. In other words, it's business intelligence that fuels daily (or thereabout) decision-making. Regardless of your position or industry, having to make decisions informed by stale data is an enormous roadblock to success. Operational business intelligence is defined as real-time business intelligence derived from ongoing business operations. In other words, it's business intelligence that fuels daily (or thereabout) decision-making. Regardless of your position or industry, having to make decisions informed by stale data is an enormous roadblock to success.

    Learn the advantages of InetSoft's small footprint BI platform.

    What Are Some Examples of Operational Business Intelligence for a Credit Operations Analyst?

    Operational business intelligence for a Credit Operations Analyst involves using data and analytics to gain insights and improve the efficiency and effectiveness of credit-related operations within an organization. Here are some examples of operational business intelligence initiatives that a Credit Operations Analyst may undertake:

    1. Credit Application Processing Time: Analyzing the time taken to process credit applications can help identify bottlenecks or inefficiencies in the credit approval process. By tracking and measuring the processing time, the analyst can identify areas where the process can be streamlined, such as automating certain steps, reallocating resources, or improving communication channels.

    2. Credit Approval Rate: Monitoring the credit approval rate helps evaluate the effectiveness of credit risk assessment and decision-making processes. The analyst can identify factors that contribute to higher rejection rates, such as inconsistent credit policies, inadequate underwriting criteria, or incomplete documentation. This analysis enables them to recommend improvements to enhance the approval rate without compromising risk management.

    3. Credit Portfolio Performance: Analyzing the performance of the credit portfolio provides insights into credit quality, delinquency rates, default rates, and overall portfolio health. The analyst can identify trends, patterns, and risk concentrations within the portfolio, enabling them to make informed decisions regarding credit risk management, collections strategies, or loan restructuring initiatives.

    4. Aging of Receivables: Monitoring the aging of receivables helps assess the effectiveness of credit collection efforts. The analyst can analyze the aging buckets to identify accounts that require prioritized collections efforts or potential write-offs. This analysis facilitates the development of targeted collection strategies, such as implementing reminder campaigns, modifying payment terms, or initiating legal actions when necessary.

    5. Customer Credit Utilization: Examining customer credit utilization patterns provides insights into how customers are utilizing their credit limits and whether they are nearing their credit capacity. The analyst can identify customers who may be at higher risk of default due to excessive credit utilization or changes in their repayment behavior. This analysis helps in proactively managing credit limits, establishing risk mitigation measures, or initiating customer outreach for risk assessment.

    6. Credit Fraud Detection: Leveraging data analytics and machine learning techniques, the analyst can develop models to detect and prevent credit fraud. By analyzing historical data and identifying patterns indicative of fraudulent activities, the analyst can build predictive models that flag suspicious transactions, unauthorized credit applications, or account takeovers. This proactive approach helps mitigate credit fraud risks and protect the organization's financial interests.

    7. Customer Creditworthiness Assessment: Utilizing operational business intelligence, the analyst can develop credit scoring models to assess the creditworthiness of customers. By analyzing various customer data points, such as credit history, income levels, and repayment behavior, the analyst can develop models that provide insights into the risk associated with extending credit to different customers. This analysis helps optimize credit decision-making processes and minimize the risk of default.

    8. Operational Efficiency Metrics: Apart from credit-specific metrics, the analyst can also track operational efficiency indicators, such as turnaround time for customer inquiries, accuracy of credit data entry, or completeness of credit documentation. Monitoring these metrics helps identify areas for process improvement, employee training needs, or system enhancements to enhance overall operational efficiency.

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    What KPIs Does a Credit Operations Analyst Use?

    1. Collection Effectiveness: This KPI measures the effectiveness of credit collection efforts. It can include metrics such as the percentage of outstanding receivables, the aging of receivables, or the percentage of accounts in collections. The analyst can track these metrics to evaluate the performance of collection strategies, identify areas for improvement, and optimize collection processes.

    2. Credit Loss Ratio: This KPI measures the ratio of credit losses (such as bad debts or write-offs) to the total amount of credit extended. It provides insights into the overall credit risk and helps the analyst evaluate the effectiveness of credit risk management strategies. Monitoring this KPI helps identify trends, assess portfolio performance, and refine underwriting criteria to minimize credit losses.

    3. Customer Satisfaction: While not specific to credit operations, customer satisfaction is an important KPI to gauge the overall experience and perception of customers regarding credit processes and services. Feedback mechanisms, surveys, or Net Promoter Score (NPS) can be used to measure customer satisfaction. The analyst can track and analyze customer feedback to identify areas for improvement and enhance customer-centricity in credit operations.

    4. Operational Efficiency Metrics: These KPIs assess the operational efficiency of credit processes. Examples include the average time to respond to customer inquiries, accuracy of credit data entry, turnaround time for credit limit changes, or completeness of credit documentation. By monitoring these metrics, the analyst can identify process bottlenecks, allocate resources effectively, and streamline operational workflows.

    5. Compliance Metrics: These KPIs evaluate the organization's adherence to credit-related regulations, policies, and industry standards. They can include the number of compliance violations, regulatory fines or penalties, or the completion rate of compliance training programs. Monitoring compliance metrics helps the analyst ensure that credit operations align with legal and regulatory requirements.

    6. Non-Performing Loans (NPLs): NPLs are loans that are past due and in default or at risk of default. They indicate borrowers who are experiencing financial difficulties or are unable to fulfill their repayment obligations. Monitoring the percentage of NPLs in the credit portfolio helps identify potential credit risks and assess the overall credit quality.

    7. Delinquency Rate: The delinquency rate measures the percentage of loans or accounts that are past due but not yet in default. It indicates the number of borrowers who are experiencing payment delays. Tracking the delinquency rate helps identify early warning signs of potential credit problems and assess the effectiveness of collection strategies.

    8. Default Rate: The default rate measures the percentage of loans that have defaulted, typically defined as loans that are severely delinquent and unlikely to be fully repaid. It represents loans that have failed to meet repayment obligations, leading to a significant loss for the lender. Monitoring the default rate helps assess credit risk and evaluate the effectiveness of credit risk management strategies.

    9. Charge-Off Rate: The charge-off rate measures the percentage of loans or credit balances that the lender has written off as uncollectible. It reflects the losses incurred by the organization due to defaults or severe delinquencies. Tracking the charge-off rate helps assess credit risk, evaluate the accuracy of credit risk assessments, and make provisions for potential losses.

    10. Loan Loss Reserve Ratio: The loan loss reserve ratio measures the amount of money set aside by the organization to cover potential loan losses. It represents the provision made to account for credit risks within the portfolio. Monitoring this ratio helps ensure that sufficient reserves are allocated to mitigate potential losses and maintain financial stability.

    11. Credit Concentration: Credit concentration refers to the extent to which credit exposures are concentrated within specific industries, geographic regions, or customer segments. Assessing credit concentration helps identify potential vulnerabilities and diversification needs within the portfolio. It ensures that the organization is not overly exposed to specific sectors or risks.

    12. Recovery Rate: The recovery rate measures the percentage of the loan or credit balance that is recovered after default or charge-off. It reflects the organization's ability to recover funds through collection efforts, asset liquidation, or legal actions. Monitoring the recovery rate helps assess the effectiveness of collection strategies and evaluate the overall recovery performance.

    13. Portfolio Mix: Portfolio mix refers to the distribution of credit exposures across different types of loans, credit products, or customer segments. Assessing the portfolio mix helps evaluate the risk profile of the portfolio and ensure a well-diversified credit portfolio. It helps manage concentration risks and align the portfolio with the organization's risk appetite and strategic objectives.

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    View live interactive examples in InetSoft's dashboard and visualization gallery.

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