Credit scoring is an automated, objective and consistent tool which helps lenders to provide quick loan decisions. It can replace some of the more mechanical work done by experienced loan officers whose decisions are intuitive but potentially subject to bias. Prospective borrowers may have a strong motivation to fraudulently falsify one or more of the attributes they report on their application form. Applicants learn about the characteristics that are used to build credit scoring models, and may alter the answers on their application form to improve their chance of loan approval. Few automated credit scoring models have considered falsified information from borrowers. We will show that sometimes it is profitable for financial institutions to spend money and effort to identify dishonest customers. We will also find the optimal effort that banks should spend on identifying these liars. Furthermore, we will show that it is possible for liars to eventually adjust their lies to escape from credit checks. The proposed issue will be studied using simulated data and discriminant analysis. This research can help lending financial institutions to reduce risk and maximize profit, and it also shows that it is feasible for customers to lie intelligently so as to evade credit checks and get loans.

In this paper, we overview a new approach to comparing execution traces. Such comparison can be useful for purposes such as improving test coverage and profiling system's users. In our approach, traces are compressed into different levels of compaction and are then compared iteratively from highest to lowest levels, rejecting dissimilar traces in the process and eventually leaving residual, similar traces. These residual traces form an important feedback for improvement or analysis goals. The preliminary results show that the approach is scalable for industrial use.

Many researchers have emphasized the importance of documenting assumptions (As) underlying software requirements (Rs). However, As and Rs can change with time for reasons such as: (i) an A or R was elicited incorrectly and subsequently needs to be changed; (ii) operational domain changes induce changes in the A and R sets; and (iii) the change in validity of an A, or desirability of an R, respectively, causes the validity of another A or desirability of an R to change. In Section 2, we describe our model and how it works. To put such a model into practice, we need to consider at least two scenarios. One is intra-release cycle-time, where invalidity risk is predicted at the start of the project for times between the inception and completion of the project. This would give us intra-release risk trends. The second scenario is prediction over multiple releases. This would give us a risk trend over a longer period of time. The full paper describes an algorithm to cover both of these scenarios and gives an example (from a banking application) of how the model could apply in practice. Here, we consider only the first scenario due to limitation of space.