AI in Decision Making 

Could AI make strategic business decisions? In theory, adding AI to decision-making frameworks could give us better efficiency. But could it take the onus of making strategic decisions?  

AI can process vast amounts of data at unprecedented speed. The ability to analyze multiple data points simultaneously makes the technology an invaluable asset in strategic planning and decision-making. 

Assume there’s a loan applicant, and for credit risk assessment, there are multiple documents, financial statements, and numbers to peruse. In such a context, AI can potentially fast-track the document and financial statement analysis.  

It’ll then offer you the findings on a dashboard to validate. Though there are regulations in place for such decision-making by AI, it truly presents a paradigm shift.

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AI Making Decisions 

Let’s pick a few key areas in finance where decision-making must be apt and how AI is playing an important role in it. 

AI in Credit Underwriting 

Many banks have been using AI to assess the creditworthiness of customers and small businesses. Lenders use AI/ML along with traditional scoring methods to predict default risk.  AI/ML models are also able to accommodate alternative data such as online footprint, utility payment, etc. 

Initially, techniques like OCR and natural language processing aided lenders in reading financial statements and extracting insights from loan applications. More recently, large language models have been playing a key role. For instance, not only can Gen AI extract information, but it can also summarize and analyze the findings for human analysts for perusal. 

AI in Fraud Detection

The way fraud has evolved in the last five years, AI has to play an important role in arresting it. AI has made existing fraud much more complex. A study found that phishing scams generated by AI achieved significantly better results. Not only did this study show that AI could run this scam on a much larger scale but also at a fraction of the cost. 

On the other hand, AI-charged fraud tactics also evade traditional fraud detection methods. Synthetic identity fraud threatens the financial system, where fraudsters are combining legitimate IDs with synthetic data from platforms like Only Fake. So, AI is needed. 

Already, ML excels at detecting fraud by recognizing patterns across massive transaction datasets. Banks are also introducing supervised learning models on labeled examples of fraudulent and legitimate transactions to spot known fraud patterns. Unsupervised learning models, on the other hand, help detect anomalies to flag unknown fraud tactics. 

A major benefit has been reducing false positives. Traditional rule-based systems often cast a wide net that inconveniences customers (e.g., declining a valid card purchase). AI’s pattern recognition is more precise, meaning far fewer customers get needless fraud calls and compliance teams spend less time on false alarms. 

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AI in Risk Management

The risk spectrum in banking and finance is complex, comprising credit, liquidity, market, and operational risks. AI models, with their superior prediction techniques and utilization of large volumes of data, are being leveraged for risk management for more efficient decision-making.  

In credit risk management, beyond the initial underwriting decision, AI is used for ongoing portfolio monitoring and early warning systems (EWS). Machine learning models analyze internal data (e.g., borrower payment behaviors, account balances) alongside external data (macroeconomic trends, news, social media sentiment) to detect signs of credit deterioration much earlier than human analysts might. 

This gives lenders substantial lead time to mitigate emerging risks (e.g., working with a troubled borrower) before a loan default occurs. In market risk and trading, AI techniques like reinforcement learning and advanced analytics are being tested for scenario analysis and stress testing. AI can simulate thousands of market scenarios to assess a portfolio’s value-at-risk, or detect subtle patterns in market data that signal changing volatility. 

Meanwhile, operational risk management benefits from AI by detecting anomalies in internal processes – for instance, using NLP to flag suspicious email communications that might indicate compliance breaches (rogue trading or Libor manipulation clues), or computer vision to monitor physical security risks. 

AI in Regulatory Compliance

Regulation compliance in banking and finance spans across AML, KYC, transaction reporting, trade surveillance, regulatory findings, and more. These manpower-heavy functions are paving the road for new AI-driven solutions: RegTech. These solutions automate and augment compliance-heavy work. 

Read more: AI for Regulatory Compliance: Making it Easier for Enterprises to Remain Compliant ‍

For example, anomaly detection algorithms flag transactions that deviate from a customer’s normal behavior or mirror known laundering typologies. AI can also help automate the generation of Suspicious Activity Reports (SARs) by analyzing customer profiles and transactions and drafting narrative reports for compliance officers. 

So far, so good, but there are a few caveats! 

Across all the functions mentioned above, AI is proving to be a game-changer. It’s enabling decisions that are faster, scalable, and data-driven. AI in credit underwriting has been helping onboard ‘good customers.’ These models are helping weed out fraudulent activities while RegTech helps comply with critical regulations. All different kinds of risks are being managed and mitigated with the help of AI. 

Financial institutions have a lot to gain from AI, but it requires a strategic framework for deployment. In a regulated field like banking, AI poses several risks. While performing credit underwriting, for instance, AI that’s trained on flawed data could perpetuate bias against certain communities or people of colour. 

Read more: Mitigating AI Bias: A Guide for Business Leaders ‍

Here are a few key recommendations for :

1. Embed AI in a Strong Governance Framework: Treat AI models as you would any critical process – with clear ownership, risk controls, and oversight. 

2. Invest in Explainability and Talent: Build explainability into AI solutions so that outcomes can be understood and communicated. Simultaneously, invest in talent – upskilling current staff and hiring AI specialists who also grasp financial services. 

3. Start Small, Then Scale: Many successful AI deployments began as pilots in a narrow area, delivering a quick win, and then scaled up. 

4. Monitor and Adapt: AI implementation is not a one-and-done project – it’s an ongoing capability. Set up continuous monitoring of model performance, outcomes (including tracking for biases), and emerging external changes. 

5. Leverage Partnerships and Innovation: Banks don’t have to do everything in-house. The fintech and tech vendor ecosystem for AI is rich – from large players to specialized organizations like Arya.ai.  

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Conclusion

AI has immense potential to enhance decision-making in financial institutions by making it faster, smarter, and more scalable. In the coming years, those institutions that successfully integrate AI into their decision fabric will likely set themselves apart in efficiency and customer satisfaction, while those that lag may find themselves at a competitive and regulatory disadvantage. 

The message to business leaders is clear: AI is here to stay in finance, and the time to embrace it thoughtfully is now – balancing innovation with prudence will be the key to harnessing AI’s full potential in credit, fraud, risk, and compliance. 

If you’d like to discuss the prospect of leveraging AI or autonomous finance solutions for your financial institution, connect with Arya AI (An Aurionpro Company).