Artificial Intelligence and the importance of data 

 AI is all about understanding the data. AI tries to decipher various patterns inside the data and the relationship between different entities, and it tries to either predict an event or generate more data.

In earlier years, with the help of ML and deep learning, AI was used to predict the possibility of an event. AI is used to capture leading indicators of an event, and based on past data, it is used to indicate the Future of any other event.

Now, with Generative AI, AI models get trained on tons of data. Various methods like Deep Learning, Attention, and transformer models are used here.

With a transformer model, AI can generate new data types. Input data can be text, image, audio, or any other format. On similar lines, Output data can be text, photo, audio, or any other format.

Goal of a Bank

For any bank, these are the primary goals.

1. Ability to collect money at the lowest cost. This can be in the form of a CASA account or deposit account. 
2. Ability to lend money to the borrower at a profitable rate to the bank.
3. Ability to offer other financial products and collect Fees.

While these are primary goals, banks also must make sure that they have an eye on

1. Their practices adhere to all the regulatory guidelines. 
2. Making sure that money lent by them comes back as per agreed terms and conditions.
3. The correct type of liquidity is maintained in the bank so that the bank can return money to depositors whenever applicable. For example, this means that short-term deposit money is given for short-tenure loans. Similarly, long-term deposit money is only provided for long-term loans.

Combining all these, a bank should focus on these points to become successful.

A .Make sure that they can participate in every customer’s various journeys. They should be present, able to offer help in his personal and professional life and help him grow. 

This can include many things like helping a customer buy a house, helping the customer in case he or his family member wants to pursue higher education, buying a car, or helping him export or import items required for his business.

B. Make sure that they can adhere to all the regulator rules.

C. Make sure their cost is the lowest while doing the above things.  

It’s clear from the above points that banks need to become trusted partners of their customers and become trusted and transparent actors for the regulator.

Trust comes with data, and that’s where banks have the upper hand.

Banks have lots of data about a customer.

Once a customer opens an account and maintains that account, there are various events where the bank captures data.

Data gets collected during

1. Onboarding 
2. During KYC and AML checks
3. Nominee and family data
4. Education qualification data, 
5. Job and salary data 
6. Daily spend. 
7. Overall saving data
8. In case of any cross-sell, data related to Credit Card Usage, Investment Data
9. If the customer takes any loan from a bank, much data related to that loan (in turn connected to the customer’s various habits or businesses) gets collected.
10. In case the customer buys or sells anything, data related to getting collected.

All these points are valid for any retail customer. In the case of corporate customers, there are many more events when data is collected.

Banks have witnessed a significant shift in operations, customer relations, and services in the past few decades thanks to Artificial Intelligence,

This technology, mainly through its subsets of Machine Learning (ML) and Generative AI, has been instrumental in reshaping how banks approach age-old challenges and opportunities.

Historical Perspective

The early eighties financial world was vastly different from today’s world. 

For example, less than 10% of all trading was conducted through computer systems.

The primary trading methods were traditional brokers, floor trading, and telephone calls.

However, as technology began to advance, so did the means of trading. With the onset of the 1990s and the subsequent rise of personal computers, trading began to see a shift towards digital platforms.

In the early 2000s, the transformation was real.The burgeoning power of the internet revolutionized countless sectors, and finance was no exception. Almost overnight, the industry saw a surge in electronic trading.

By this time, nearly 70% of all trades were executed electronically. This shift wasn’t just about convenience; it also brought greater efficiency, transparency, and speed to the trading world.

Now, let’s pivot to the present era. With the dawn of Artificial Intelligence (AI), trading has experienced yet another profound metamorphosis.

High-frequency trading (HFT), powered by AI algorithms, has become the dominant force in the equity market. This method, which involves making many trades in milliseconds, has become so prevalent that it now constitutes over 50% of all equity market volume.

So, within a few decades, we have moved from manual to AI-driven HFT. This demonstrates the relentless pace of technological advancement and its impact on the banking ecosystem.

Technical Underpinnings of AI in Finance

1. Neural Networks: In 2019, a study revealed that 27% of global banks used neural networks for risk assessment, showcasing the growing trust in this technology.

2. Deep Learning: Research suggests that deep learning models could help increase fraud detection accuracy by up to 20%.

3. Transformer: A model architecture at the core of most state-of-the-art ML research.

The transformer is made of several layers with multiple sub-layers.

Here, two main sub-layers are the self-attention layer and the feedforward layer. 

Self-attention is similar to how we humans read paragraphs. We remember the previous sentence in a paragraph and the last word in a sentence and connect the following sentence or word while reading.

We weigh the importance of each word in a sequence to determine the meaning of that sentence.  

In the same way, the self-attention layer also looks at and derives the importance of each word in the sentence, while the feedforward layer applies non-linear transformations to the input data. 

With the help of these layers, the transformer learns and understands the relationships between the words in a sentence. 

Banking Landscape in the Future

The current banking horizon is being redefined with Artificial Intelligence. 

Integrating Artificial Intelligence into the banking sector is not just a ‘fashionable trend.’ It will bring a paradigm shift that redefines how we think about money, investments, and financial services.

This is about more than embracing technology for the sake of modernity. There’s a very tangible incentive behind this mass transition.

The Future isn’t just about technology; it’s about a better, more innovative, and more inclusive financial world for all.

1. Enriched Customer Experience: Beyond Traditional Services

A. Chatbots and Virtual Assistants: Chatbots have been there for some time. But now, with generative AI, chatbots can get trained on external and internal bank and customer data. This will help chatbots in answering in a very personalized way. It’s different for all customers.

But based on the customer profile and his recent transactions (say his ATM card has not worked, hence this customer is angry and is calling the bank’s support center), a chatbot can provide an answer in a personalized way.  

Chatbot can help with the language diction, which can calm the customer. With GEN AI & ML-powered chatbots, swift, accurate, real-time personalized responses can be given, which will be a win-win situation for everyone. Banks save on operational costs, and customers receive quicker service.

B. Personalized Banking: The one-size-fits-all paradigm in the banking sector is fading. GEN AI and ML algorithms can use individual transaction data to customize financial advice, suggest relevant products, or even notify users about unusual spending behavior.

One survey shows that 83% of consumers are open to sharing their data for personalized experiences.

2. Fortified Risk Management:

Here we can leverage AI’s Predictive Powers

A. Adaptive Fraud Detection: With global card fraud losses hitting billions of dollars in 2022, fraud detection is paramount. ML provides a robust solution by examining millions of transactions in a couple of seconds, detecting suspicious patterns, and raising instant alerts.

B. Revolutionizing Credit Scoring: The conventional metrics for determining credit scores have often been scrutinized for not accurately depicting an individual’s financial stability. Enter AI and ML. They analyze many data points, even non-traditional ones, ensuring more holistic credit assessments.

3. Investment and Trading: The Algorithms Are Taking Over

A. Algorithmic Trading: The stock market scene has been dramatically altered with the onset of high-frequency, ML-based trading. 

B. Robo-Advisors: The ascent of these algorithm-driven investment platforms has been nothing short of spectacular. 

4. Process Automation: The Drive for Efficiency

A. Seamless Customer Onboarding: Generative AI’s capability to parse through documents, seamlessly perform KYC verifications, and enroll customers can compress a process that once took days into mere minutes.

B. Optimized Predictive Analysis: Financial institutions now deploy ML to anticipate cash withdrawal patterns at ATMs, ensuring they’re always adequately stocked.

This ensures customer satisfaction and significantly trims the overheads linked with cash logistics.

5. Financial Forecasting: Crunching through data and underlying sentiments 

By sifting through expansive datasets, ranging from various social media content and sentiment analysis to global macroeconomic indicators, Generative models and ML have the prowess to make astoundingly accurate market movement predictions.

Hedge funds around the globe are increasingly leaning on these AI models, often finding them outperforming traditional forecasting methods.

6. Bank’s opportunity to Learn and Delight (BOLD): With generative AI, banks can learn about the customer and various global events practices and offer personalized products for each customer—an absolute delight. 

7. Summarizing lengthy documents: Customers often need to go through long documents in the bank. Now, these documents can be uploaded into GEN AI-powered chatbots, and customers can ask this chatbot relevant questions.

GEN AI-powered chatbot can offer a summary of the document and provide answers to all his questions. This can help customers quickly know all the terms and conditions mentioned in the document.

8. Making underwriting easy: Similar to the abovementioned point, bankers must also go through various documents often while underwriting. Now, all that data can be extracted and provided in a question-answer format or any other way suitable to the banker.  

9. Knowledge Management and Training: With GEN AI, bankers’ training can be personalized. With GEN AI, models can be trained on historical data (financial market collapse, currency wars, different trade and supply chain issues etc..) and all relevant data related to that bank. This will ease onboarding issues for employees joining the bank and help lateral employees of the banks.

10. Regulatory compliance: With GEN AI models, bank employees can be briefed about the latest regulations. It will also help banks ensure that all their practice reports adhere to the law.

11. Financial Inclusion: Often, bank people need to learn how to deal with a person with special needs or who doesn’t have a formal credit history. With GEN AI, those situations can be created during the training of the bank employees, and necessary education can be provided.

On Similar lines, with the customer’s consent, his other data can be taken to arrive at his credit score. Practices like these will help in increasing financial inclusion.  

12. Low Code-No Code Platform with GEN AI: With GEN AI and LC-NC platforms, bank IT persons can make software changes and roll out various features. 

Along similar lines, the LC-NC platform with GEN AI can help with various tools for banks. Corporate customers of banks can use it to generate templates and documents.  

Examples of artificial Intelligence in financial markets

1. Enriched Customer Experience:

JPMorgan’s COiN (Contract Intelligence Platform): In banks, processing underwriting documents was a painstakingly laborious process that demanded precision and vast human hours.

JPMorgan’s COiN, leveraging the prowess of AI, now processes these documents in mere seconds, an endeavor that traditionally consumed thousands of hours. This incredible efficiency minimizes errors and saves considerable costs, positively impacting JPMorgan’s financial health.

Bank of America’s Erica: Erica, an AI-driven virtual assistant, has transformed the way Bank of America interacts with its customers. A virtual financial assistant that is always ready with personalized insights, it has done more than 1.5 billion interactions since its inception. 

2. Fortified Risk Management:

Mastercard Decision Intelligence: Fraud prevention remains a paramount concern for financial institutions.

Mastercard’s Decision Intelligence is a real-time authorization decision solution that looks at thousands of data points, applies cutting-edge modeling techniques to each transaction, and arrives at a single transaction decision score.

This helps the issuer fine-tune the authorization decision. 

3. Investment and Trading:

BlackRock’s Aladdin (Asset, Liability, and Debt and Derivative Investment Network):  A comprehensive investment management and trading platform, it uses sophisticated risk analytics along with comprehensive portfolio management.  

Numerai: The concept of crowdsourcing has found a lucrative application in the financial sector. Numerai is an AI-run, crowdsourced hedge fund set to revolutionize quantitative finance. 

4. Process Automation:

DBS Bank’s Jim (Jobs Intelligence Maestro): Recruitment is a critical yet time-consuming process for any institution. DBS Bank’s AI tool, Jim, brings efficiency into recruitment. 

5. Financial Forecasting:

AlphaSense: In the fast-paced world of finance, timely information is crucial. AlphaSense helps its users find company information and provides research data within a second. 

Kensho: Kensho is the AI and innovation hub for S&P Global. It works with natural language data, including complex document speech data and machine learning models. 

Apart from the names mentioned above, many other banks like Capital One, NatWest, Raiffeisen Bank, Commonwealth Bank, Goldman Sachs, ING, Citigroup, TD Bank, UBS, Capital One, Royal Bank of Canada, Wells Fargo, Commonwealth Bank of Australia are using AI in their operation. 

The Flip Side: Challenges in the AI-driven Financial World

For all its merits, AI’s integration in finance isn’t devoid of hurdles:

1. Data Privacy Concerns: An inevitable offshoot of a data-driven approach is the growing apprehension over potential data misuse or breaches. We must acknowledge that each citizen has a fundamental right to privacy and data protection. Institutions need to adhere strictly to regulations like GDPR and CCPA.

 2. Ethical Implications: AI systems, if not properly calibrated, can inherit biases, which could result in discriminatory practices like biased loan decisions or credit approvals. Banks need to be very careful here while training their AI models. 

We also need to address the concerns related to ‘low-quality data,’ ‘propensity to hallucinate,’ ‘Integration with the overall ecosystem,’ and ‘Availability of talent.’  

Conclusion

Artificial Intelligence, in its many avatars, will surely change the banking landscape. It’s a real breakthrough technology. But like any technology, it can act only as an enabler. It’s up to us to harness the power of this technology and bring positive changes in the financial world.

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• Source: https://www.finextra.com/blogposting/25361/artificial-intelligence-in-banking?utm_medium=rssfinextra&utm_source=finextrablogs