Here is the challenge; why is consumer or organisation x getting credit and is consumer or organisation y not getting this creditworthiness? Interesting question especially when both of them have the same parameters and are requesting this credit from the public domain sector. In fairness all applications should be able to globally be judged with the same set of rules and standards that determine the credit score.
Today, human intelligence is shaping artificial intelligence and, increasingly, artificial intelligence is starting to shape human intelligence. When the impact of AI systems increases, more people need to be able to understand their workings and effects. To achieve this, we need to be able to augment human intelligence to allow us to interact with various specimen of intelligent systems in sustainable terms. Black boxes need to be uncovered to enable co-agency and collaboration on a wider scale. “Democratizing” artificial intelligence would allow more people to create diverse ways to design and develop new approaches to intelligent systems. Just like coding or media literacy are seen as today’s essential skills, being able to comprehend and affect intelligent systems will be an essential skill for tomorrow. Trust will be key.
What is a good credit score? A good credit score is crucial for financial success. A credit score is a three digit number calculated from your data-rich credit report and is one factor used by lenders to determine your creditworthiness for a mortgage, loan or credit card. Your score can affect whether or not you are approved as well as what interest rate you are charged. A good credit score is generally considered to be 720 or higher. Lenders, however, can each have different standards for what they consider to be a good credit score, so currently it still is important to keep building your score to receive the most favorable interest rates and highest rates of credit approval. In essence an current breakdown of the credit score shows that the influential subjects are; 35% payment history, 30% amounts owed, 15% length of payment history, 10% new credit and 10% is about the credit mix.
What are the most common questions? Here are, in my opinion, the five questions regarding the credit scoring mechanism that’s in place when a company is performing an acceptance test regarding potential customers. It is very important that these questions are explained clearly every time this subject is being touched upon. This prevents frustrated customers and delivers a good customer experience with your business. Here are the five key consumer questions;
1. What is the reason for rejecting my application?
2. In the past I have had issues with payments. Why is this still following me?
3. The registration is not correct, what should I do next?
4. Why aren’t I accepted if I have never had any debts?
5. The credit worthiness test, isn’t that done through the Credit Registration Office?
What is the advantage for a business using a ledger? Consumers should know where their credit score stands before they apply for credit so they can build their score and better their chances for approval. In the Netherlands lenders must sign any loan from Credit Registration Office (BKR). That is a legal obligation. We offer organizations, businesses and government the ability to store the (credit score) models that are determining creditworthiness in the public domain in a distributed ledger. By storing these models secured and encrypted in a immutable and permanent ring of nodes other third parties have the ability to quickly verify and audit the workings of these generic credit models.This way we offers customers a secured and publicly acceptance regarding trust and transparency into the workings and blueprint of the credit score models. These models can be updated as often as every hour, with no hidden fees and always available. These models could also perform a set of ethical or business actions which become alive as a result of smart contracts that are attached to a transaction slash model.
Once you, as customer or client with customers, know why your credit score is approved or rejected your organization is sharing trust. Alvis could be used for building valid trust that can be audited at any date and time in the insurance tech, new fintech businesses and government related services. We aim to make it easier for the types of “predictive” models that need to be publicly available as in how they work but also publicly available for auditing capabilities so that any person with granted access can verify, validate and audit all connected models. If giving these blocks of information correct it will preventing getting frustrated customers and contributes to a good customer experience with your business or civil service. The solution creates an immutable audit trail that parties along the chain can refer to in order to establish authenticity, offering valuable reassurance about the provenance of data and decisions. The distributed ledger is permanent and immutable storing the models and is giving secured verification for organizations and companies. It is a trust builder. The data within Alvis is stored in the ISO 15022 and ISO 20022 formats, which provide guidance for the distillation of financial information into machine-readable formats.