A Systematic Review on AI in Finance: Search Strategy

Artificial intelligence applications in finance span algorithmic trading, credit risk assessment, fraud detection, and robo-advisory services, each representing genuinely distinct research literatures with different typical study designs, different relevant databases, and different appropriate appraisal approaches for a systematic review.

Narrowing to a specific financial application

A review examining AI-based credit risk assessment accuracy is asking a fundamentally different question than one examining algorithmic trading performance or fraud detection effectiveness, and committing to one specific application area at the protocol stage prevents an unmanageably broad evidence base spanning genuinely unrelated financial AI applications.

Diagnostic and predictive accuracy framing for many finance AI questions

Many AI-in-finance questions, particularly fraud detection and credit risk assessment, are structurally diagnostic or predictive accuracy questions similar to AI in medical diagnosis discussed elsewhere on this site -- evaluating how accurately a model identifies fraudulent transactions or predicts default risk against a known outcome. Where this applies, QUADAS-2 or PROBAST, rather than standard intervention-focused risk-of-bias tools, are the appropriate appraisal choice, and extraction should capture the accuracy data these tools and any planned meta-analysis require.

Algorithmic trading as a genuinely different kind of question

Reviews examining algorithmic trading performance typically involve a different evidence structure entirely, often comparing algorithmic strategy performance against market benchmarks or human trader performance over a defined period, raising distinct methodological questions about how performance was measured, over what market conditions, and with what genuine out-of-sample validation, rather than the diagnostic accuracy structure relevant to fraud or credit risk questions.

Search strategy across finance and technical literature

AI-in-finance research appears across finance and economics journals, technical machine learning venues, and industry or working paper literature specific to quantitative finance, given how much relevant early-stage research in this commercially sensitive field circulates as working papers before formal publication. A comprehensive search needs to span databases like EconLit and Business Source Complete alongside technical AI literature and relevant working paper repositories.

Publication bias considerations specific to this field

Financial AI research, particularly algorithmic trading research, carries a well-documented risk of selective reporting given strong commercial incentives to publish or promote only successful strategies while unsuccessful approaches go unreported. This is worth addressing explicitly and considering carefully in your review's discussion, since this specific field's publication bias risk may be more pronounced than in many other AI application areas.

Terminology variation across this literature

This field uses inconsistent terminology across academic finance research and more applied industry or practitioner literature -- "algorithmic trading," "quantitative trading," "machine learning trading strategies," and specific technique names are used with varying precision depending on the source, and a comprehensive search strategy needs to account for this terminology range rather than assuming standardized language.

Data availability and reproducibility considerations

Financial AI research often relies on proprietary or restricted-access market data, meaning reproducibility -- a standard expectation discussed in this site's broader open science guidance -- is sometimes genuinely more difficult to achieve in this field than in areas with more open data traditions. Noting this as a specific limitation where it applies to your included studies adds honest context to your review's findings.

A practical starting point

Before finalizing your search strategy, confirm which specific financial AI application your review addresses, whether this structures as a diagnostic or predictive accuracy question requiring QUADAS-2 or PROBAST, and which combination of finance-specific and technical databases your comprehensive search genuinely needs to span given this field's particular publication landscape.

Considering regulatory context as background, not evidence

Financial AI applications increasingly operate within evolving regulatory frameworks specific to algorithmic trading and automated financial decision-making, and while this regulatory context is genuinely useful background for your review's introduction and discussion, it remains distinct from your primary empirical evidence base, and should be presented as context rather than blended into your synthesized findings about AI system effectiveness or accuracy.

Practical considerations for accessing proprietary research

Some of the most commercially significant AI-in-finance research may never be published at all, given how directly this research connects to a firm's competitive advantage, and acknowledging this as a genuine, structural limitation of any systematic review on this specific topic, rather than presenting your synthesized findings as though they capture the full state of relevant knowledge, reflects honest awareness of this field's particular evidence landscape.

A closing consideration on this topic's stakes

Given how much real capital and real risk is involved in financial AI applications, a rigorously conducted, honestly limited systematic review still offers genuine value to practitioners and regulators navigating this space, even where the full underlying evidence base remains partially inaccessible. Being transparent about this limitation, rather than implying a more complete picture than genuinely exists, is what keeps this kind of review credible to informed readers.

A final word on serving a genuinely practical audience

Investors, regulators, and financial institutions all have real, practical interest in trustworthy synthesis on this topic, and writing your discussion section to genuinely serve this practical audience, translating technical findings into clearly stated practical implications, extends your review's real-world usefness well beyond a purely academic contribution.

Considering risk alongside performance throughout

Financial AI research often emphasizes performance metrics without equal attention to risk or failure modes, and deliberately extracting and synthesizing risk-related findings alongside performance findings, where your included studies report them, gives readers a more complete, more genuinely useful picture than performance metrics presented in isolation. That fuller picture is ultimately what makes a financial AI systematic review genuinely trustworthy to readers who understand how incomplete a purely performance-focused account would be. That honesty earns lasting credibility in a field prone to overstatement, particularly given how much genuine financial risk sits behind real institutional decisions. A careless or overstated conclusion could genuinely mislead a reader making a consequential financial decision, which is why this standard is worth holding consistently across every section of the manuscript, from the earliest framing question through the very last sentence. Readers relying on this work to inform real financial decisions deserve nothing less.