Analysing the role of technology in compliance and regulatory structures
Bibliographic record
Abstract
This paper does not set out to be an expert treatise on RegTech or FinTech, or any of the other catchy compound words that have entered our daily lexicon. It simply outlines one organisation’s journey in determining the need for a technological solution for a myriad of compliance obligations. The firm in which the author works has eight (soon to be nine) offices around the world, with two in Australia, and one in the UK, Germany, Switzerland (applying for registration), South Korea, Hong Kong, Japan and the USA. There are some group entities also licenced in Canada and the Cayman Islands. Some regimes require more than one licence (eg, five in Australia alone). The subsequent regulatory burden is significant. Add to that a global insurance programme, a global taxation programme, a global risk management framework, a global governance framework plus the day to day burden of simply running a business — the compliance obligations are huge. Yet, on number of staff alone, the present author’s organisation is nothing like some of the massive financial behemoths around the world, a potential cause of compliance management headaches. This paper is about IFM Investors’ experience in implementing technology into the business to become more effective, and less intrusive on the front line teams when it comes to meeting firm-wide compliance tasks. In doing so, IFM Investors had some learnings along the way from which readers might avoid some of the challenges faced.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".