 "Data-Silos")
Companies have become increasingly concerned with accurately assessing their business health in recent years. In today's rapidly changing business environment, traditional methods such as relying solely on one year's worth of a business's financial data are no longer adequate.
The new 360-degree real-time risk model addresses this need by taking a holistic approach that uses both lagging and leading indicators to evaluate various risks such as financial, cyber, ESG, social, operational, regulatory, global, reputational, and competitive. It calculates these risks by taking into account real-time input parameters, including banking transactions, business loans, insurance premiums, e-commerce transactions, payments data, commercial real estate, telecom, utilities, insurance, tax filing, and firmographic data, to give a more accurate view of the company's business health.
The real-time indicators are used to calculate a business credit score, which is determined by standardizing the variables for each parameter and evaluating them using a neuro-decision AI model. The resulting score is then compared to known parameters for both delinquent and very successful businesses to determine decision-making cut-off points.
What sets this new method apart is its highly adaptable nature. The decision-making model used to generate the business credit score is fine-tuned for each business, sector, or geography based on the data fed into the model, allowing for highly customized assessments of a company's financial health.
If the resulting score is either too high or too low, the specific trends of input parameters should be examined to determine the root cause of the deviation. For example, if the overall score is too low and at the same time insurance-weighted variable appears to be out of the regular range, it may be necessary to assess whether changes in claims, payments, or employee count for the insurance parameter are contributing to the deviation. This in turn may provide key insights into the business risk of the entity.
With this data, decision-makers can make more informed choices in supplier onboarding, working capital lending to SMBs, and other partnership decision-making processes, and have greater confidence in the accuracy of their assessments.
This new method has the potential to revolutionize how businesses evaluate their business health (not just financial health) and make critical business decisions. More research is needed to fully comprehend the potential impact of this new method on business.
However, one obstacle to implementing data privacy measures is the presence of "Data Silos", where data is not shared among different entities. To overcome this, the government should encourage data sharing between government departments and private organizations across industries. The Indian government and industry bodies can then establish a secure data framework to address the challenge of data sharing and exchange.
1. Establish a legal framework: The government needs to create a legal framework that outlines guidelines and regulations for data sharing among entities and industry bodies, ensuring that the process is secure, transparent, and in accordance with data protection laws.
2. Promote a culture of active data sharing: The government and industry bodies must foster a culture of data sharing by disseminating its benefits, thereby creating a conducive environment for data sharing, which is critical to the implementation of the business credit score system.
3. Develop robust data storage and sharing infrastructure: The government and industry must work together to create a secure and efficient data sharing and storage infrastructure for accurate and up-to-date credit score data.
4. Establish a centralized data exchange platform: A centralized data exchange platform can be established to enable entities and industry bodies to securely share data using a variety of API, flat file, or other protocols, and to access that data in accordance with data privacy and use-case policies, ensuring that the data is not misused in any way.
5. Institute a balanced Data Privacy & Protection measures: The government and industry need to establish strong data privacy measures, such as encryption and firewalls, to protect credit score data from breaches. Regulations similar to GDPR should be in place to ensure security while allowing for innovation.
By implementing secure data measures and promoting data sharing, the Indian government and industry can establish a trustworthy credit score system that is accurate and up-to-date. Say goodbye to outdated methods and welcome the era of real-time, 360 degree risk assessments.
Authored by Asit Kumar Sahoo, Senior Commercial Product Manager at Flexport, Expertise in IT Product Management having worked across diverse sectors & technologies