How much effort should be spent to detect fraudulent applications when engaged in classifier-based lending?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it