The OYI Review · One Young India Press

AI in Finance: Beyond Trading Bots

Published 2025 · Reviewed and updated 2026 by One Young India Review

Abstract

Every day, millions of people grapple with financial decisions that could shape their futures. A small business owner waits weeks for a critical loan approval. A young professional pays high fees for basic investment advice. A farmer cannot access credit without a traditional paper trail. These are not mere inconveniences. They represent significant barriers to economic opportunity that Artificial Intelligence (AI) is well positioned to help remove. The scale of the problem is large and measurable: the World Bank estimates that around 1.4 billion adults remain unbanked, and the International Finance Corporation puts the formal micro, small and medium enterprise financing gap in developing economies at roughly 5.2 trillion US dollars a year.

The core challenge is a dilemma that traditional financial services have long faced: how to provide personalised, intelligent financial guidance to millions of individuals while keeping costs reasonable and access universal. Historically, banks have solved this through segmentation, where wealthy clients receive personal attention and everyone else is offered standardised, one size fits all products.

This dynamic is now changing. AI is poised to disrupt this paradigm by making sophisticated financial tools more accessible. When AI helps a lender assess the creditworthiness of someone without a formal credit history, or when it enables fraud detection that does not incorrectly block legitimate transactions, technology becomes a bridge to financial inclusion. This paper examines how AI is reshaping the financial landscape through four applications: personalised investment management, intelligent fraud detection, inclusive algorithmic trading, and natural language processing that makes finance more accessible. It also sets out the risks that must be managed, drawing on the 2024 assessment by the Financial Stability Board and the Reserve Bank of India FREE-AI framework of 2025.

Key Findings

AI powered portfolio management is lowering the cost and minimum size of professional investing. Automated advisers commonly charge around 0.25 per cent of assets a year with account minimums that range from zero to a few hundred rupees or dollars, against roughly 1 per cent for a traditional human adviser who often requires a much larger opening balance.
Fraud detection that learns individual behaviour, rather than applying fixed rules, can cut false alarms while catching genuine fraud. Vendor reported reductions in false positives are large, though independent, standardised public benchmarks remain limited.
Natural language processing is breaking down the informational barrier between complex financial products and everyday understanding, including across Indian languages.
The most significant challenge is not technological. It is ensuring that these powerful tools are built and deployed to serve everyone fairly. Evidence shows that algorithmic lending can narrow, but does not automatically remove, discrimination.

1. The Current Problem

Financial services have long operated on a simple yet exclusionary principle: sophisticated advice costs money to deliver, so only high net worth clients typically receive it. As a result, most people receive standardised products and limited, generic guidance that fails to address their circumstances.

This model creates real gaps in opportunity. Small businesses wait weeks for loan decisions that modern systems could make in minutes. Individual investors often rely on generalised advice when their situations call for customised, dynamic strategies. Conventional fraud systems frequently block legitimate transactions in order to catch suspicious ones, frustrating customers and eroding trust.

The Human Cost

The International Finance Corporation estimates that about 70 per cent of micro, small and medium enterprises in emerging markets lack adequate financing to grow, an unmet need that stifles jobs and innovation.
Individual investors typically underperform market benchmarks because of poor timing, emotional decisions and fees. In a landmark study, Barber and Odean found that the households that traded most actively earned about 11.4 per cent a year while the market returned about 17.9 per cent over the same period.
Legitimate customers are inconvenienced when rule based systems block valid transactions in order to stop a smaller number of fraudulent ones, a trade off that damages trust.
Access to quality financial advice remains concentrated among urban and affluent populations, perpetuating economic disparities. Globally, around 1.4 billion adults still have no account of any kind.

AI offers a different path, one where sophisticated financial intelligence becomes a more widely available tool rather than a premium reserved for those who can afford it.

2. Four Ways AI is Transforming Finance

2.1 Personalised Investment Management

The old way: the traditional approach uses risk questionnaires that sort people into three broad categories, conservative, moderate or aggressive, followed by standardised portfolios that rarely account for personal nuance.

The AI way: modern systems consider many dynamic factors, from spending patterns and career trajectory to life goals and market conditions, to build and maintain more personalised strategies.

An illustrative example: consider Anita, a teacher in Pune who wants to buy her first home in five years. A basic robo adviser labels her a moderate risk investor and recommends a standard balanced portfolio. A more capable AI system goes deeper. It analyses her spending to estimate her real savings capacity. It considers local property trends in Pune to set a more realistic down payment target. It factors in the typical salary progression for teachers in her region. It creates time aware recommendations that shift automatically as her goal approaches. Crucially, it explains the plan in plain language: "Because you want to buy a home in five years, we begin with growth focused investments. As your target date nears, the portfolio will move toward safer, capital preserving options to protect your down payment."

The impact: automated advice has sharply reduced the price of entry. Published fee data show automated advisers charging in the region of 0.25 per cent of assets a year, with account minimums ranging from zero to a few hundred, compared with roughly 1 per cent and much higher minimums for many traditional advisers. Lower costs and smaller minimums mean that professional grade, rules based investing is no longer limited to the wealthy. These are averages, and real outcomes still depend on markets, product quality and investor behaviour.

2.2 Intelligent Fraud Detection

The challenge: traditional fraud systems rely on rigid, predefined rules. This produces high rates of false positives that block legitimate transactions, while often failing to adapt to new fraud patterns.

How AI helps: instead of applying static rules, AI systems learn what normal looks like for each customer. By building a behavioural baseline, they can flag genuine anomalies with greater accuracy.

An illustrative case: Rajesh runs an electronics store in Jaipur. Each weekday morning he checks his business account and pays three regular suppliers, always from his phone on his shop Wi-Fi. The system learns these patterns: timing, location, device and amounts. When someone tries to access his account at 2 am from another city on an unfamiliar laptop, the system flags a high risk anomaly, not because a fixed rule was broken, but because the behaviour is inconsistent with his established pattern.

The results: institutions that deploy behavioural systems report meaningful falls in blocked legitimate transactions, better detection of real fraud and fewer related customer complaints. Exact figures vary by vendor and are not always independently audited, so they should be read as indicative rather than guaranteed. The direction of travel, fewer false alarms and better detection, is consistent across the industry.

2.3 Algorithmic Trading That Benefits Everyone

The common misconception: high frequency trading (HFT) is often seen as a tool that benefits large institutions at the expense of individual investors.

The reality: in many cases these algorithms support a healthy market by providing liquidity and helping to keep pricing efficient. Reviewing the academic evidence, work published through the Commodity Futures Trading Commission finds that computerised and high frequency trading tends to improve traditional measures of market quality, narrow bid ask spreads and aid price discovery, although the same studies caution that liquidity can thin during periods of stress.

How it works: when you place an order through a retail trading app, market making algorithms help ensure that a counterparty is usually available at a fair price, that the bid ask spread stays narrow, which lowers trading costs, and that your order is executed quickly at a competitive price. New information is reflected in prices rapidly, improving price discovery.

Beyond speed: modern trading algorithms are not only about speed. They analyse many inputs at once, including price movements, news sentiment, macroeconomic data and other signals, to make decisions in fractions of a second.

Market impact: the result, on balance, is a more efficient market with better price discovery and improved liquidity that benefits investors large and small. This benefit is conditional, and regulators continue to monitor the risks that automated trading can pose in volatile conditions.

2.4 Natural Language Processing

The barrier: financial services are notorious for jargon that intimidates ordinary people. Terms such as expense ratio, amortisation and duration risk create a wall between financial expertise and the people who need it most.

The solution: AI powered natural language processing can translate these concepts into simple language, tailored to each person's level of financial literacy.

Real time intelligence: these systems can process large volumes of documents daily, from earnings reports and news articles to regulatory filings, and turn unstructured data into clearer insights for investors.

Multilingual power: advanced systems can synthesise information from diverse sources, reading agricultural policy news in Hindi, regional bank coverage in Tamil and international reports in English to build a more complete view. This matters in a country where account ownership has risen quickly but financial literacy and comfort with English vary widely.

Example in action: when a pharmaceutical company reports clinical trial results, an NLP system can analyse the announcement for signals of success or failure, cross reference it with historical data from similar trials, assess the likely effect on the share price, identify related companies that might be affected, and generate alerts for investors holding the stock. As with any model, these outputs are estimates and should support human judgement rather than replace it.

3. Applications in Practice

The scenarios below are illustrative composites that reflect patterns documented in the sources cited. They are written to show how AI is being applied, not as audited case studies of named institutions.

3.1 Widening Rural Credit

One of the clearest inclusion gains comes from alternative data credit scoring. Where a farmer or micro entrepreneur has no formal credit history, lenders increasingly assess creditworthiness using non traditional signals such as mobile and digital payment records, platform activity and other verifiable data. The International Finance Corporation reports that this approach is helping lenders recognise economic activity among thin file borrowers who were previously invisible to formal credit systems, and that women borrowers often perform comparably or better under such assessments. The practical effects, where it is done responsibly, are faster decisions, more approvals and access to formal credit at rates well below those charged by informal moneylenders. The size of the opportunity is large: the unmet financing need for MSMEs in emerging markets runs into trillions of dollars.

3.2 Reducing Fraud Friction

Behavioural fraud detection changes the customer experience as much as the security outcome. Consider a business traveller such as Priya, who frequently makes international purchases. A rule based system may block her card on each foreign transaction, while a system that has learned her travel and spending pattern lets legitimate transactions proceed and reserves intervention for genuine anomalies. The benefit is measured not only in fraud caught but in the friction removed for honest customers, which is where much of the value of AI lies in this domain.

3.3 Lowering the Cost of Advice

The democratisation of investment follows the same logic. AI powered wealth platforms can offer personalised explanations in multiple languages, dynamic rebalancing and behavioural coaching at low minimums and low fees. The core mechanism is well documented: automated advice cuts the marginal cost of serving each additional customer, which is why fees near 0.25 per cent and minimums near zero are now common. Whether any individual platform beats a given benchmark depends on markets and product design, so investors should judge each service on its disclosed, verified track record rather than on marketing claims.

4. Managing the Risks

4.1 The Explainability Challenge

AI models, particularly deep learning systems, can operate as black boxes, producing results without a clear account of how they were reached. This is unacceptable in finance, where transparency is essential and, increasingly, expected by regulators.

The solution: apply Explainable AI techniques that provide meaningful, human understandable reasons.

Weak: "Your loan application was declined based on an analysis of 247 factors."

Strong: "Your application was declined because of a high debt to income ratio, 65 per cent against our preferred maximum of 40 per cent. Paying down existing debt or adding a co-signer would strengthen a future application."

4.2 Bias and Fairness

AI systems learn from historical data. If that data reflects past societal biases, the model can perpetuate or amplify discrimination. This is not hypothetical. Research summarised by the National Bureau of Economic Research, based on the study by Bartlett, Morse, Stanton and Wallace, found that algorithmic lending reduced discrimination against minority borrowers compared with face to face lending, and removed discriminatory rejection, yet still left minority borrowers paying measurably higher interest rates. Technology alone did not deliver fairness.

Mitigation strategies:

Regular auditing: continuously test models for discriminatory patterns against protected groups.
Representative training data: ensure datasets are large and reflect all customer demographics.
Fairness constraints: build fairness metrics into the model objective during development.
Human oversight: keep a person in the loop for sensitive decisions such as final loan approvals and fraud investigations.

4.3 Systemic Risk

A newer category of risk emerges when many institutions adopt similar AI models for trading or risk management. This can create correlated, pro cyclical behaviour that destabilises markets under stress. In November 2024 the Financial Stability Board assessed exactly this concern, identifying third party and service provider concentration, market correlations, cyber risk, and model risk and data governance as vulnerabilities that AI could amplify at a system wide level, and noting that generative AI raises the potential for fraud and disinformation in markets.

Risk management:

Model diversity: encourage a range of AI approaches across institutions to prevent herd behaviour.
Regulatory monitoring: supervisors must track the aggregate deployment of AI across the system, as the Financial Stability Board recommends.
Advanced stress testing: run tests that simulate how interconnected AI systems might behave in a crisis.

5. Implementation Roadmap

Phase 1: Foundation, Months 0 to 6

Assess readiness: evaluate the quality of existing data infrastructure and technical capability.
Choose a focus: start with a specific, high value application such as fraud detection or a customer service assistant.
Build capability: establish data governance and begin recruiting or training AI talent.
Plan pilots: select controlled, low risk settings for initial testing.

Phase 2: Controlled Deployment, Months 6 to 18

Run pilots: deploy with rigorous human oversight and parallel testing against existing processes.
Monitor performance: track both technical accuracy and business metrics such as satisfaction and cost savings.
Address issues: refine the system based on real world feedback.
Prepare to scale: document learnings and create a playbook for wider rollout.

Phase 3: Strategic Expansion, Months 12 and beyond

Scale what works: expand proven applications across business units.
Integrate systems: connect AI with core banking and customer relationship systems.
Train teams: build AI literacy across the organisation.
Improve continuously: establish ongoing monitoring, retraining and optimisation.

In India this roadmap now has a clear policy backdrop. The Reserve Bank of India FREE-AI committee report of 2025 sets out guiding principles and recommendations for the responsible and ethical use of AI across banks, non banking financial companies and fintechs, giving institutions a reference point for governance, consumer protection and assurance.

6. The Path Forward

The integration of AI in finance is not about replacing human judgement with machine logic. It is about widening access to financial intelligence that was previously available only to a select few.

The opportunity: when a farmer in a remote village can get a fair credit assessment in hours instead of weeks, when a young professional can access personalised strategies with a small monthly contribution, and when legitimate transactions flow smoothly while fraudulent ones are stopped accurately, technology becomes a genuine force for economic inclusion.

The responsibility: this transformation must be managed thoughtfully. The same systems that can broaden access can also perpetuate bias or create new risks, as both the Bartlett research and the Financial Stability Board make clear. Real success requires not only technical sophistication but a steady commitment to fairness, transparency and human impact.

Looking ahead: the institutions that thrive in the coming decade will be those that use AI to become more human, not less, delivering guidance that helps people build the futures they want. The future of finance lies in a partnership of artificial and human intelligence in service of human prosperity. Success will be measured not by the sophistication of the algorithms but by the tangible improvement in the financial lives of real people.

7. Recommendations

For Financial Institutions

Start customer first: prioritise applications that solve genuine customer problems.
Invest in explanation: deploy systems that can justify their decisions in clear terms.
Build gradually: begin with well defined use cases and scale on proven, measurable results.
Maintain human oversight: AI should augment, not replace, the trusted relationships at the heart of finance.

For Regulators

Enable innovation: create flexible regulatory sandboxes that encourage beneficial AI while managing risk.
Focus on outcomes: regulate for fair results such as non discrimination rather than prescribing specific technologies, in line with the outcome based approach of the RBI FREE-AI framework.
Promote collaboration: encourage information sharing on best practice, risk management and ethics, and monitor system wide AI adoption as the Financial Stability Board advises.

For Customers

Stay informed: understand how AI is being used in the services you receive.
Ask questions: request clear explanations for any AI driven recommendation or decision that affects you.
Diversify: AI is a powerful tool, but do not rely entirely on automated advice for complex, life altering decisions.

Conclusion

AI in finance represents a real opportunity to build a more inclusive financial system, one where more people, regardless of wealth or location, can access intelligent, personalised guidance. The applications examined here show AI's potential to solve concrete problems: making rules based investing affordable, detecting fraud without frustrating honest customers, keeping markets efficient, and lowering the language and literacy barriers that keep people from understanding their options.

Achieving this vision requires a balance between innovation and responsibility. As finance becomes more intelligent it must also become fairer, more transparent and more inclusive. The evidence is encouraging but not automatic: algorithmic lending narrows discrimination without erasing it, automated advice lowers costs without guaranteeing returns, and system wide AI adoption brings new stability risks that supervisors are only beginning to map. The question is no longer if AI will reshape finance, but how we will guide that reshaping toward outcomes that benefit everyone, not only those who were already well served.

Sources

Cite this paper

Aarush Dandekar (2025). AI in Finance: Beyond Trading Bots. The OYI Review, One Young India Press. https://www.oneyoungindia.com/white-papers/ai-in-finance-beyond-trading-bots

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