Abstract: Techniques are provided for tracking a number of transactions-of-interest in a transaction-recording blockchain using a control blockchain. A transaction-of-interest is a transactions that is in a particular state. A request to perform an action is received. Upon receiving the request and determining that the action corresponds to a new transaction-of-interest, a control blockchain is checked to determine the current number of transactions-of-interest in the transaction-recording blockchain and maximum allowed number of transaction-of-interest for the transaction-recording blockchain. In response to determining that the current number of transaction-of-interest in the transaction-recording blockchain are less than the maximum allowed: allowing the action to occur, adding a new block to the transaction-recording blockchain, and updating the control blockchain to indicate the new number of transaction-of-interest.

Abstract: Techniques are provided for forming clusters of individual prediction targets (IPTs). An initial prediction target is a target for which an automated prediction has been generated. IPTs may be, for example, borrowers to which a lending entity has extended loans based on predictions generated by a credit policy. Each cluster includes (a) a “core” of underperforming entities (UEs), and (b) a set of boundary performant entities (PEs). The UEs that belong to the UE core of a cluster are “similarly situated” relative to the values of their features. For example, in the context where the IPTs are borrowers, the UEs at the core of a cluster may correspond to defaulting borrowers that had similar bureau data, lending entity data, and borrower data. The boundary performant entities of the cluster may be borrowers that have not defaulted, but had similar credit qualifications as the UEs of the cluster.

Abstract: Techniques are provided for tracking a number of transactions-of-interest in a transaction-recording blockchain using a control blockchain. A transaction-of-interest is a transactions that is in a particular state. A request to perform an action is received. Upon receiving the request and determining that the action corresponds to a new transaction-of-interest, a control blockchain is checked to determine the current number of transactions-of-interest in the transaction-recording blockchain and maximum allowed number of transaction-of-interest for the transaction-recording blockchain. In response to determining that the current number of transaction-of-interest in the transaction-recording blockchain are less than the maximum allowed: allowing the action to occur, adding a new block to the transaction-recording blockchain, and updating the control blockchain to indicate the new number of transaction-of-interest.

Abstract: Techniques are provided for forming clusters of individual prediction targets (IPTs). An initial prediction target is a target for which an automated prediction has been generated. IPTs may be, for example, borrowers to which a lending entity has extended loans based on predictions generated by a credit policy. Each cluster includes (a) a “core” of underperforming entities (UEs), and (b) a set of boundary performant entities (PEs). The UEs that belong to the UE core of a cluster are “similarly situated” relative to the values of their features. For example, in the context where the IPTs are borrowers, the UEs at the core of a cluster may correspond to defaulting borrowers that had similar bureau data, lending entity data, and borrower data. The boundary performant entities of the cluster may be borrowers that have not defaulted, but had similar credit qualifications as the UEs of the cluster.

Abstract: Techniques are described for predicting the likelihood that a loan default will occur. The technique can be performed pro-actively, in order to predict situations in which a loan default is likely even before any payment has been missed on the loan. Upon detecting a high likelihood of default, the loan default prediction system may automatically execute remedial actions. For example, the loan default prediction system may automatically generate an offer, to the borrower in question, to allow the borrower to skip the next loan payment. The technique may also be used to generate accurate financial health scores that take into account trends in a borrower's activities. The actions that are automatically performed based on the financial health scores may include both remedial actions and reward actions. The outcomes of the actions may be fed back into the system to further refine the model used thereby.

Abstract: Techniques are provided for testing policy modules for bias. Policy modules are software modules that generate lending decisions based on information about loan applicants. The techniques involve performing multiple testing iterations based on each test case. For example, in one iteration, values for all input parameters of the policy module may come from the test case. That iteration produces a “baseline” lending decision. During other iterations, the values for most input parameters do not change. However, for the one or more input parameters that correspond to the characteristic for which bias is being tested, the input values are changed from iteration to iteration. For example, when checking for age bias, the age of a loan applicant may be varied with each iteration. The lending decisions generated based on each test case are collectively referred to as a “sibling batch” of lending decisions.