jurgendn Banking AI Literature Mapper
Build a source-grounded map of banking and fintech AI methods, successful cases, datasets, benchmarks, and evaluation practices.
The goal is to help an industry R&D team justify solution directions with credible precedent while staying honest about transfer risk, regulatory constraints, and data differences.
When to use this skill
Use this skill when the user asks for:
- prior successful AI/ML cases in banking or fintech;
- papers supporting a banking solution proposal;
- method options for credit, fraud, AML, customer, document, compliance, or operations problems;
- evaluation benchmarks or datasets for banking-like tasks;
- a literature-backed internal R&D proposal.
If the user asks for a broad non-banking research survey, use literature-triangulation. If the user needs a final solution design, use publication-grounded-solution-design after mapping sources.
Workflow
1. Define the banking problem shape
Classify the problem by both domain and technical shape.
Domain examples:
- credit risk;
- fraud/scam detection;
- AML and suspicious activity monitoring;
- collections and recovery;
- customer churn and personalization;
- contact-center and branch operations;
- document understanding and KYC;
- compliance, risk controls, and audit;
- employee productivity and knowledge retrieval.
Technical shapes:
- tabular prediction;
- temporal forecasting;
- ranking or triage;
- graph detection;
- anomaly detection;
- causal uplift;
- recommender systems;
- information extraction;
- retrieval and grounded generation;
- workflow/process mining;
- optimization.
Search using both labels. Banking labels alone may miss transferable methods from insurance, telecom, e-commerce risk, healthcare operations, cybersecurity, or public-sector fraud.
2. Separate source types
Build separate buckets:
- Banking/finance papers: closest domain match.
- Adjacent-domain papers: similar problem shape with stronger methods or benchmarks.
- System/case papers: production or near-production deployments.
- Datasets/benchmarks: public tasks useful for baselines or proxies.
- Surveys: maps of the area, not direct evidence.
- Regulatory/explainability sources: constraints that affect adoption.
Do not mix these as equal evidence.
3. Evaluate evidence quality
For each source, assess:
- publication venue or credibility;
- dataset realism;
- sample size and time span;
- evaluation protocol;
- baselines;
- leakage controls;
- explainability and fairness treatment;
- operational relevance;
- whether the result is reproducible or only claimed.
Flag weak evidence explicitly.
4. Map methods to banking transfer assumptions
For every method family, state what must be true in the bank:
- data fields exist and are reliable;
- labels are not too biased by old policies;
- decisions and outcomes are timestamped;
- entities can be linked consistently;
- model outputs can be explained to users or auditors;
- latency and integration constraints are feasible;
- compliance approves the intervention.
5. Extract evaluation patterns
Identify how the literature evaluates similar work:
- temporal train/test split;
- out-of-time validation;
- precision@k under review capacity;
- cost-sensitive metrics;
- calibration;
- fairness by segment;
- human-in-the-loop evaluation;
- backtesting;
- ablations;
- robustness to drift or adversarial change.
Recommend the evaluation style that best matches the user's problem.
6. Produce a reading and action map
Prioritize sources into:
- must-read;
- useful for methods;
- useful for evaluation;
- useful for risk/compliance;
- weak or only background.
Then recommend how to use the literature:
- justify a pilot;
- select baselines;
- avoid known failure modes;
- define data requirements;
- draft the method section.
Output format
# Banking AI literature map: [problem]
## Problem classification
- Banking domain:
- Technical shape:
- Decision/workflow:
- Key constraints:
## Source map
| Source | Type | Problem match | Method | Evidence quality | Evaluation pattern | Transfer assumptions | Caveats |
|---|---|---|---|---|---|---|---|
## Method families
### [Method family]
- Core idea:
- Best supporting sources:
- When it fits:
- When it fails:
- Banking transfer assumptions:
- Evaluation implications:
## Prior successful cases
## Datasets and benchmarks
## Evaluation patterns to reuse
## Gaps and risks in the literature
## Recommended reading order
## How this should feed solution design
Quality bar
A good banking literature map should make it clear which sources are strong enough to support an R&D proposal, which only provide inspiration, and which assumptions must be tested before deployment.