The Future of Finance: Embracing AI in Financial Services


Artificial Intelligence (AI) is transforming many industries. While early, it shows tremendous potential for innovation & efficiency. From enhancing customer service to automating complex processes, AI is becoming an integral part of experimentation, and modern finance isn’t behind. This blog delves into the various applications of AI in finance, the challenges it presents, and its promising future.

AI in Finance: A Paradigm Shift

AI encompasses machine learning, natural language processing (NLP), and computer vision, which are driving significant changes in finance. This transformation is akin to Industrial Revolution. Many believe, AI will be as transformative as Electricity.

“Just as electricity transformed almost everything 100+ years ago, today I actually have a hard time thinking of an industry that I don’t think AI will transform in the next several years.” – Andrew Ng

This belief has led to – some might say a “gold rush” – where effective AI implementation will set new industry leaders apart. It has also brought up opportunity to bridge gap between Davids and Goliaths of the industry. Naturally, Infra companies are benefitting tremendously from this at the moment as shovel sellers benefitted during gold rush. Below chart sums up the craze.

Understanding AI

AI simulates human intelligence through learning, reasoning, and self-correction. One of the ways to classify technologies within AI is shown below. Some classify GPT/BERT within Deep Learning but that’s still not settled yet given multiple pieces of puzzle involved.

Another way to classify AI is by the level of use as listed below:

  • Narrow AI: Specialized for specific tasks (e.g., Siri, Alexa).
  • General AI: Capable of performing any intellectual task a human can do.
  • Superintelligent AI: Surpasses human intelligence.

“Machine intelligence is the last invention that humanity will ever need to make.“  –  Nick Bostrom

Machine Learning and its Impact

Machine learning, a subset of AI, involves feeding data into algorithms that improve over time based on detected patterns. Supervised learning maps labelled data to known output. The goal is to generate formula based on input and output values. Whereas, Unsupervised Learning explore patterns and predict the output. Reinforcement Learning follows a trial and error method. In Reinforcement Learning an agent learn through delayed feedback by interacting with the environment. AlphaGo and AlphaZero, developed by DeepMind, have demonstrated superior performance in complex games like Go, chess, and Shogi by training entirely through reinforcement learning – learning from scratch by playing against itself and adjusting strategies based on wins and losses. This showcases potential for solving other complex problems in logistics, robotics, and finance.

AI in Natural Language Processing and Computer Vision

  • Natural Language Processing (NLP): GPT (Generative Pre-trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers) have revolutionized how machines understand and generate human language. These models handle tasks like translation, summarization, and question-answering with unprecedented accuracy. They’ve paved the way for more sophisticated conversational AI and more effective information retrieval systems.
  • Computer Vision: The development of more efficient and powerful convolutional neural networks (CNNs) has significantly improved image and video analysis capabilities. Techniques like few-shot learning, where models learn from a very small amount of data, are opening new avenues for applications in medical imaging and real-time surveillance.

Fintechs have been eating banks for long time. Below excerpts show the rapid shift of financial institutes to tech-enabled businesses, It will only accelerate from here.

Real-World Applications of AI in Finance

1. Client-Facing AI Chatbots

AI-powered chatbots and virtual assistants provide instant customer support, handling large volumes of queries and offering personalized financial insights across various channels. For instance, Bank of America’s Erica and HSBC’s Amy are excellent examples of how AI chatbots can enhance customer service by providing 24/7 support and resolving common inquiries promptly​.

2. Personalized Finance

AI offers personalized banking experiences by tailoring financial advice and services to individual customer profiles. This improves customer engagement and loyalty. For example, Betterment and Wealthfront use AI to provide personalized investment strategies based on individual financial goals and risk tolerance​.

3. Risk Management

AI enhances risk management by analyzing vast amounts of financial data to identify and mitigate risks. JPMorgan Chase’s COiN platform reviews legal documents and manages risks, while AI systems in companies like CitiBank help detect fraudulent activities and prevent financial crimes​.

4. Cybersecurity

AI improves cybersecurity by detecting and responding to threats in real-time. AI systems can identify unusual patterns and potential security breaches quickly, enhancing the overall security posture of financial institutions.

5. Predictive Analytics

AI models forecast market trends, enabling better investment decisions. Predictive analytics helps in understanding future market behaviors based on historical data, thus aiding in strategic planning and decision-making​.

6. Compliance

AI ensures adherence to regulatory requirements by monitoring transactions and flagging any discrepancies. It helps financial institutions stay compliant with evolving regulations by automating routine tasks and generating reports, which reduces the chances of human error and enhances efficiency.

7. Fraud Detection

AI systems detect fraudulent activities by analyzing transaction patterns. They can identify and prevent fraud more effectively than traditional methods. For example, Feedzai collaborates with CitiBank to flag suspicious payments and protect against fraud​.

8. Sentiment Indicators

AI analyzes social media and news to gauge market sentiment. Sentiment analysis helps investors understand market emotions and make informed decisions, providing a competitive edge in trading and investment strategies.

9. Trading Signals / Algorithmic Trading

AI-driven algorithms execute trades based on market data analysis. Companies like Trumid and AlphaSense use AI for real-time pricing intelligence and trading decision support, making trading more efficient and profitable​.

10. Asset Allocation

AI optimizes asset allocation by analyzing market conditions and individual investment goals. This ensures a balanced and diversified portfolio, enhancing overall investment performance.

11. Portfolio Management

AI enhances portfolio management by providing real-time insights and automated rebalancing. Robo-advisors like Wealthfront and Betterment offer automated portfolio management services tailored to individual risk profiles and financial goals​.

12. Credit Scoring / Direct Lending

AI improves credit scoring by analyzing a wide range of data points, leading to more accurate and fair credit assessments. This helps financial institutions offer better loan terms and reduce default rates.

13. Debt Collection

AI streamlines debt collection processes by predicting repayment behaviors and optimizing communication strategies. This leads to higher recovery rates and more efficient debt management​.

14. Real-Time Insurance Premiums

AI enables real-time adjustments of insurance premiums based on dynamic risk assessments. This ensures fair pricing and better risk management for insurers and customers alike​.

15. Real-Time Claims Approval

AI accelerates the claims approval process by quickly analyzing and verifying claim details. This improves customer satisfaction and reduces operational costs for insurance companies​.

Challenges in Implementing AI in Finance

Deploying AI in finance poses several challenges:

  • Data Privacy and Security: Ensuring the protection of customer data is paramount.
  • Bias and Fairness: Addressing biases in AI models is critical to avoid perpetuating existing inequalities. Imagine critical loans getting rejected due to biases in training data. We have seen effects of this issue in Gemini.
  • Regulatory Compliance: Adhering to financial regulations while implementing AI systems requires robust governance frameworks.
  • Explainable AI: Many advanced machine learning models, especially deep learning networks, are often described as “black boxes” because their decision-making processes are not easily understandable to humans. This lack of transparency can be problematic in critical applications. Developing methods to make these models more interpretable is crucial for gaining public trust and for regulatory compliance.
  • Legal Implications: As AI systems become more autonomous, legal systems will need to adapt to issues around AI personhood, liability, and intellectual property rights. Determining who is responsible when an AI system causes harm or how intellectual property created by AI is owned are complex questions that require new legal frameworks.
  • Technological Unemployment: While AI and automation can lead to more efficient business processes and economic growth, they also pose the risk of significant job displacement across various sectors. Developing policies that support workforce transition, such as universal basic income, retraining programs, and changes to education systems, are vital to address these challenges.

Future Prospects

The future of AI is promising, with advancements expected in several areas:

  • Energy-Efficient ML Models: With the environmental impact of training large-scale AI models becoming a concern, there has been a push towards developing more energy-efficient algorithms and hardware optimized for AI computations. This includes new types of neural network architectures that require less computational power to train and run.
  • Quantum Computing: Combining machine learning with quantum computing has opened new potentials for processing information at unprecedented speeds. Though still in early stages, quantum machine learning promises to solve complex computational problems much faster than classical computers.
  • Advanced AI Models: Will enhance decision-making capabilities and efficiency.
  • Human-AI Collaboration: AI will complement human roles, leading to more efficient and accessible financial services​.
  • Integration: Making use of AI models and infrastructure and applying them to business use cases will be vital skill set. This is low-hanging fruit for India & perhaps should be focused on.

Encouraging Innovation in Academia

To foster innovation, students and colleges should:

  • Learn: Upskill in AI technologies.
  • Innovate: Choose cutting-edge projects for academic or corporate projects.
  • Take Initiative: Intern at companies working on AI technologies. Suggest such elements to the project if they are not already part of the scope.
  • Contribute: Participate in open-source libraries that are part of AI ecosystem.

Shankh by InfinityPool: A Case Study

At Shankh, we leverage AI for various use cases:

  • Risk Profiling: Tailored investment strategies based on individual risk profiles.
  • Predictive Analytics: Forecasting market trends.
  • Trading Signals: Algorithmic trading based on AI predictions.
  • Sentiment Indicators: AI-driven insights into market sentiment.
  • ChatBot: Robo-advisor which brings everything together for customers.

AI is reshaping the financial services industry, making it more efficient, accurate, and personalized. By integrating AI into their operations, financial institutions can create a future that is more inclusive and fair. Embracing AI, collaborating across disciplines, and pushing the boundaries of what is possible will drive the next wave of innovation in finance.