What Are Some Potential Applications of Modular AI in Finance and Banking?

What Are Some Potential Applications of Modular AI in Finance and Banking? | OpenGrowth

Modular AI can be compared to a toolbox filled with different financial tools. Instead of one giant, unchangeable system, banks can choose the tools they need to solve specific problems. 

Has your major systems detected a breach? AI can help you with a fraud detection module to cure your security features' anomalies. 

As far as implementation is concerned, firms can take necessary steps towards the deployment of AI in finance.

Not only that, modular AI in finance and banking has the solution to most of your investment-related problems. Discover how in this article, which also explores the benefits of modular AI finance for the global economy and its prospects.

 

What is Modular AI?

With traditional AI systems, you are exposed to a more standardized set of rules. Modular AI, on the other hand, lets you pick and choose the tools you need from that toolbox.

Although it is believed to be in its initial stages, the scope for development lies just before us. Modular AI deconstructs complex AI systems into smaller, independent modules. These modules are similar to building blocks and perform specific tasks assigned to them.

Our experts have identified two different areas in which modular AI stands out. Here's a further breakdown of modular AI:

  • Top-notch flexibility: The beauty of modular AI lies in its flexibility. Financial institutions can choose the specific modules they need to address their unique challenges. 
  • Standardization: For these modules to work together seamlessly, they need to be built with standardized interfaces. That's the goal of standardization in modular AI.

 

15 Examples of How to Use AI in Banking and Finance 

Wondering which artificial intelligence-based modular platform helps automate and improve business processes? Here's a closer look at several ways in which modular AI is already having an impact:

1. Fraud detection: Trainable AI modules can identify patterns indicative of fraudulent activity. This can be further enhanced with anomaly detection modules to flag unusual spending behavior. Banks can combine these modules and come up with a powerful fraud detection system thereby minimizing losses and protecting customer accounts. Essentially, you’re enhancing data security and privacy using AI in finance.

2. Risk management: Predictive models built with AI modules access financial risks associated with loans, investments, and market fluctuations.
 

The risk button


In this way, modular AI in finance and banking can analyze vast amounts of data, including historical trends, current market conditions, and economic indicators. The result? Better capital allocation, among a plethora of things. 

3. Personalized banking: Recommender systems powered by AI modules can suggest financial products/services for a customer's specific needs and goals. For example, an AI module can analyze a customer's spending habits and income to recommend a high-yield savings account or a budget management tool. 

4. Chatbots and virtual assistants: AI modules can be used to create chatbots that answer customer queries, resolve basic issues, and even schedule appointments. This pattern can improve customer service efficiency by providing 24/7 support and reducing the burden on human representatives. However, it harms job vocations.

5. Wealth management: Robo-advisors built with AI modules can provide automated investment advice and portfolio management based on a customer's risk tolerance and financial goals. These AI-powered tools can make wealth management more accessible and affordable, particularly for individuals who don't have the resources for a dedicated financial advisor.

6. Market predictive analysis: AI modules can analyze vast amounts of market data from a variety of sources including news articles, social media sentiment, and economic indicators, By using the data procured from the AI, finance sectors can make stellar investment decisions for both individual investors and institutional asset managers.

7. Algorithmic trading: AI modules can execute trades based on pre-defined parameters and complex algorithms. This indefinitely enables faster and more efficient trading strategies, particularly in high-frequency trading environments where milliseconds can make a significant difference.

8. Know Your Customer (KYC): AI modules can automate KYC processes by verifying customer identities and documents, and speeding onboarding procedures. These series of actions not only reduce manual work for banks but also improve the customer experience by expediting account opening.

9. Credit scoring: AI modules can assess loan applications by analyzing financial data, credit history, and alternative data sources such as utility bills or mobile phone usage.

This allows for a more holistic risk assessment, leading to faster approvals for qualified borrowers and fairer lending practices. You can consider future-proofing your credit score using a few steps.

10. Anti-Money Laundering (AML): Transaction monitoring AI modules can detect suspicious activity potentially linked to money laundering. These modules are designed to detect transaction patterns, source and destination of funds, and customer profiles.

11. Sentiment analysis: AI modules can note customer interactions across various channels, such as social media, emails, and chat conversations. They do this to gauge sentiment and identify areas for improvement in products and services. Besides, banks can proactively address concerns by understanding customer sentiment.

12. Loan default prediction: AI models can assess the likelihood of loan defaults by taking on a borrower's financial history, credit score, and other relevant data points. This action allows banks to take proactive measures such as offering loan restructuring options. Other actions involve implementing early intervention programs to help borrowers avoid default.

13. Insurance underwriting: These modules can be used to automate the insurance underwriting process, leading to faster approvals. Here, AI considers factors like health history, driving records, and property details to provide accurate risk assessments.

14. Regulatory compliances: AI modules can be trained to stay updated on evolving financial regulations and identify potential compliance risks. This decision can help banks avoid hefty fines and penalties associated with non-compliance.

15. Document processing and summarization: Documentation can be a repetitive task. As for this, an AI assistant can be particularly beneficial for tasks like mortgage applications or loan agreements. These are places where a large volume of documents can be found.

 

Firms Using Modular AI in Finance and Banking

Here are some real-world examples of how AI is being used in banking and finance:

  • JPMorgan Chase & Co.: This banking giant uses AI to analyze customer spending habits and recommend personalized financial products. For example, if you're consistently putting away money for a vacation, JPMorgan Chase's AI might suggest a high-yield savings account to help your travel fund grow faster.
  • Bank of America: Ever wondered why your credit card company approves or declines a transaction seemingly instantly? Bank of America uses AI to analyze transaction data in real time, flagging suspicious activity that might indicate fraud.
  • Upstart: This fintech company uses AI to assess loan applications.
     

Fintech


By considering alternative data sources beyond traditional credit scores, Upstart can offer loan options to borrowers who may have been excluded by traditional lenders. 

  • Wealthfront: This robo-advisor uses AI algorithms to build and manage investment portfolios for its clients. Wealthfront's AI offers the option to create a diversified portfolio of stocks and bonds, automatically rebalancing it as market conditions change.
  • Barclays: If you’re looking for a faster way to transfer funds, Barclays might be the best deal. They use AI-powered chatbots to help customers with simple banking tasks, like money transfers or bill payments. These virtual assistants can elevate your banking experience. Moreover, they can also free up human customer service representatives for more complex inquiries that you may have.

 

The Financial Future Lies With Modular AI

Banks can now choose the functionalities they need, like fraud detection or automated wealth management. Just like the many advantages that come up with picking a traditional AI system, a modular approach also helps in many ways than one. 

The ultimate goal of these systems is to create a more satisfied and loyal customer base. However, building internal AI teams from scratch can be an obstacle, especially for smaller institutions. 

That's where platforms like OpenGrowth come in. Our fractional hiring model provides access to fractional executives and experts on demand and CFOs experienced in AI implementation and management, enabling businesses to tap into the power of modular AI and compete with the big players. 

With the right tools and talent, the future of finance is bright, fueled by intelligent automation and data-driven decisions. Discover the benefits of fractional on-demand experts today!

Mrittika is fascinated by the intricacies of two extremes -data science and Bollywood's vibrant storytelling. In her copywriting pursuit, she seeks perfection and is very particular about words. Beyond copywriting, she reviews cinema and shares her perspectives.

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