Transaction Profiling and Scoring Mechanism

Risk is inherent in any transaction, and the potential for fraud exists between any number of transacting parties. While certain measures can be taken to reduce such risks, each party places trust in the other to fulfill its end of the agreement. In order to mitigate this risk, we are introducing the COTI Global Trust System (GTS), which first creates a blacklist of users that are known to be tied to fraudulent activity. Secondly, we are implementing classification techniques to profile all transactions based on their associated risks. Finally, the scoring mechanism, a byproduct of transaction profiling, is a measure of trustworthiness at the user level.

The Blacklist

Blacklisting is one of the primary techniques that organizations use to safeguard the public from financial scams, malicious web pages and many other forms of Internet fraud. In our approach, blacklists identify entities that good actors should avoid transacting with. Our blacklist is continuously updated with all known fraudulent entities, so that users can fully vet them prior to transacting. Once the fraudulent entity is identified, the scoring mechanism generates a warning to inform users of the entity’s blacklisted status, thus providing the benefit of lookup efficiency.

Transaction Profiling and Classification

Every transaction has a specific pattern and this pattern recognition is the key to differentiating high risk and non-risk transactions. We leverage the information gained from past transactions associated with an address and identify similarities between new transactions and an established prototype. Any such similarity would determine a new transaction’s propensity to be risky. We use a variety of machine learning techniques to successfully perform this classification task. Transactions are analyzed based on a set of quantifiable characteristics and are then classified into specific subclasses through a distance function. Successful profiling largely depends on the degree of classification accuracy.

What is the COTI Universal Trust Score?

User trust scores can be viewed as a combination of contributing bonuses and penalties, such as contributions from successful transactions and penalties from irregular behaviour. It seems natural that each of these contributing factors would decay over time, so that more recent events have the most influence over the current user trust score. It would also be logical for different components of the trust score to decay at different rates depending on their severity. For example, if someone has committed serious fraud, then the penalty will decrease slowly with time. This is in contrast to the penalty for a small misdemeanor, which will decay faster and have a smaller long-term impact.

To compute the Trust Score of any entity, two basic components are required: feature variables and labels. The feature variables include every possible attribute that we should measure for a user at a specific point in time (e.g., prior data, actions on the network, etc.), while the labels assign a value to each of these cases. We generally think of the feature variables as determinants of data point positioning in a high dimensional space, and the labels as deciding how to illustrate it.

In the COTI GTS, we define general purpose features that are then used for the ongoing TrustScore (TS) calculation (in the past k transactions / in the past t timespan). Some of the features include:

Number of successful transactions

Number of disputed transactions (disputes lost)

Number of transactions attempted or made that do not adhere to the network’s rules (with no obvious fraudulent intentions)

Number of possible attempts to double spend/defraud. Actions like this must be critically dealt with to prevent a participant from perpetually building up a high trust score only to attempt defrauding other participants. There should either be a permanent and significant reduction in the participant’s Trust Score, or an extremely slow increase in the Trust Score.

Frequency of network use. We cannot expect ordinary users to use the network with the same frequency as merchants, so the user type is taken into account.

User type — the wide range of possible users impacts the trust score awarded to each user type and classification.

Network centrality

COTI Universal TrustScore Ranking Mechanism