The Problem AI Addresses The Technology AI Applications for Financial Services Customer Experience KYC Client Requests Personalization of Products and Services Customized Trade Recommendations and Scenario Analysis On-Demand Customized Reporting  ESG Reporting Operational Efficiency Client Service and Communication Loan Underwriting Trade Settlement and Reconciliation Risk Management Credit Risk  Event-based Risk Market Risk Compliance Ongoing KYC and Remediation Fraud and AML Monitoring Regulatory Compliance Humans + AI: The Way Forward

Author: Aditi Subbarao, Financial Services Lead

AI is bringing about a significant transformation across diverse industries, marking a notable paradigm shift. OpenAI’s GPT technology is a widely recognized example of how large language models are altering our daily experiences in ways that were not anticipated just a few years ago.

Why has AI suddenly become a topic of dinner table conversations? How can large, regulated enterprises such as financial services organizations elevate it from the realm of individual productivity to improving enterprise-scale outcomes?

The Problem AI Addresses

Financial services organizations process vast amounts of data related to customers, transactions, markets, and more, continuously. This data resides in several different systems (including hard copy documents, shared drives, electronic systems, etc.) and is in several different formats (PDFs, spreadsheets, free text, tables, visual features such as signatures and check-boxes, etc). Accessing all this data, aggregating it in a logical way, and then leveraging it to drive desired outcomes is one of the biggest pain points in large enterprises. 

Solving this pain point is key for organizations because every strategic priority hinges on leveraging that data. To drive better customer experience, operational efficiency, better risk management, and compliance, organizations need to have thorough visibility of all the relevant data. 

It is not sufficient, or efficient, to deploy human beings to solve this problem. That is far too expensive, time-consuming, and unrewarding for the people involved. A scalable, reliable, and automated solution to this problem is artificial intelligence (AI). 

The Technology

Artificial intelligence learns from data on which it has been trained and then extends this learning to make predictions. The financial services industry has used AI and machine learning for years now, but in a very different form from the present. 

Initial solutions for accessing data used templates and rules, which worked well on data in fixed formats and structures but struggled to accommodate variability. Traditional machine learning could tackle more variability, but required extensive amounts of training data and time to train. 

(See “The Ultimate Deep Learning Guide for Unstructured Data” for more information.)

The advent of deep learning and transformer models significantly reduced the requirement of training data and time. Large language models such as GPT (Generative Pre-trained Transformer) have added a whole new dimension to these capabilities – by being able to generate new content. A model trained on colossal amounts of data, coupled with enormous computational power, can now offer financial firms a juggernaut to finally address the data problem conclusively and enable value-added outcomes for colleagues and customers that were, until now, only items on wishlists. 

While the use cases and applications for this technology are near-limitless, we mention just a few examples below to serve as illustrations of the many possibilities. These are segmented into the broad areas of:

  • Customer Experience
  • Operational Efficiency
  • Risk Management
  • Compliance 

There is great power in technology, but organizations have a huge responsibility to ensure that it is used appropriately with the required checks and balances, some of which we have highlighted below. It is crucial to remember that in almost all the following use cases, an element of human review and a process of checking/monitoring the output from the AI is essential. 

Two colleagues working together.

AI Applications for Financial Services

The ability to harness data from across the organization, including data about customers, transactions, or about processes, can have an impact across all areas of the organization.  

Customer Experience

An AI-powered platform can automatically extract all incoming client communication and data, both during the initial onboarding process and on a continual basis throughout the customer lifecycle, to create a dynamic, 360-degree view of the client. Organizations can then use this data for:


Turn ongoing KYC and Client Due Diligence from a periodic event into a perpetual one updated on a daily, ongoing basis. Supplement Enhanced Due Diligence by combining data available within the bank with data from external sources. Remove friction in applying for new products by never having to ask a customer for the same information twice. Speed up onboarding and approval processes by pre-populating available information into appropriate downstream systems. 

Client Requests

Answer client queries with a highly personalized, relevant response. An AI-powered platform can help a relationship manager as she picks up a client call by providing all relevant data about the client instantly on her desktop, along with answers, suggestions, and recommendations. The relationship manager can then address any client queries, provide advice, and proactively identify any issues in real time. 

Personalization of Products and Services

AI can combine data from all previous customer interactions and bring that data to the current request to create personalized products and services for the customer. For example, AI can suggest an offer of a mortgage with variable repayments to accommodate the quarterly university fee payments the bank knows the customer makes. 

Customized Trade Recommendations and Scenario Analysis

Being able to analyze and understand a client’s current positioning and make customized trade recommendations in a rapid, real-time and dynamic way can be a huge differentiator for the trading desks of Corporate and Investment Banks (CIBs). An AI-powered platform can:

  • Query which clients hold a position, or even a trading preference, for certain securities or asset classes showing movement.
  • Identify in real time which clients would be interested in a certain position the trading desk can quote on.

Another area AI can add huge value in is scenario analysis. The regulation requires CIBs to be very explicit and clear about the potential risk a client will take on with a certain trade and how this risk evolves with market movement. AI can be used to automatically generate relevant and customized scenarios to highlight the impact of market movements on the trade. 

On-Demand Customized Reporting 

Generate comprehensive reports for customers on demand including details on their holdings, payments, accounts, activity, etc., enhanced by advice relevant to their situation and market conditions. For example, an AI-powered platform can generate a report including an analysis of the FX conversion fees the client has paid in all their travel, with an update of how much they could have saved with a specific travel money card. 

ESG Reporting

Provide investors ESG reporting on potential investment opportunities, lending opportunities, and suppliers by automatically extracting, reconciling, and validating information from a number of disparate sources, including annual reports, CSR reports, electronic systems and market data systems. When organizations input a list of queries that the report needs to focus on, AI can collate and present supporting information from across internal and external sources in a digestible format in real time.

Operational Efficiency

Operational processes involve extracting and understanding unstructured data from a variety of different sources and adapting to variable customer and risk situations in a manner compliant with a host of variable regulations and policies. Generative AI can make this process significantly faster and cheaper. 

Client Service and Communication

AI chatbots are common in customer service processes, but generative AI can make client interaction much more human-like. AI can automatically analyze incoming communication from chat, email, or phone and triage to the correct downstream systems for review. With an AI-powered platform, organizations can prioritize important requests and automatically answer a majority of queries based on relevant information. AI can provide a customer service agent with all the required information available, along with recommendations that update in real time based on the conversation. AI can also use transcripts from past calls and issues to help recommend resolution paths, thereby speeding up agent training significantly. 

Loan Underwriting

AI can generate a loan file instantly by accessing all required information about creditworthiness and collateral from multiple documents and sources. Generative AI can also provide alternative options of loan structures and associated scenario analysis, based both on the borrower’s requirements and the bank’s risk appetite. Once the human review and underwriter sign-off has been completed, AI can generate the mortgage deed or loan agreement, the contract, and other documents to close the loan.

Trade Settlement and Reconciliation

An AI-powered platform can validate and match data across orders, booking systems, termsheets, and trade confirmations in real-time. AI can also flag potential settlement breaks proactively (e.g., mismatches in dates, failed past payments, errors in required documentation for settlement, etc.).  

Risk Management

Several factors contribute to the overall risk in a bank’s book, including credit risk, operational risk, market risk, and others. Having comprehensive visibility into the data associated with these factors can help robust risk management and return generation. 

Credit Risk 

A bank can look at its entire book of existing mortgages and loans, and dynamically alter pricing to attract the type of loans it wishes to make. In mortgage lending, for example, visibility on exposure to certain locations, property types, and borrower types can enable better run-time risk management.

AI can extract from account statements and payment data of existing borrowers and correlate with incoming applications, offering the capability to determine acceptance based on risk profile. AI can highlight potential defaults and payment difficulties prophylactically, also enabling the bank to reach out proactively to offer solutions, mitigations, and advice to borrowers struggling with repayments. 

Event-based Risk

The bank can automatically do collateral and covenant management in the background even for a mortgage (e.g., sending alerts and recommended products a month before the insurance policy on a property expires, or asking for an income update when the payroll details of a borrower have changed). 

AI can query an entire database of documents for information that could expedite actioning the following use cases:

  1. Enabling event-based response by knowing exactly which loans have exposure to certain collaterals (e.g. Russian assets, or interest rates, Silicon Valley Bank shares, etc.)
  1. Knowing exactly which loans link to certain counterparties in complex borrowing structures or securitization
  1. Understanding change of exposure on a corporate action (e.g., borrower acquired by another company, borrower going bankrupt, etc.)
  1. Knowing which exposures or loans would come under certain new requirements due to potential new regulations
  1. Identifying which exposures need restructuring due to market or regulator events (e.g., Libor remediation, change of calculation agent, change or reporting frequency, etc.)
Market Risk

In addition to those one-time actions described above, an AI-powered platform can perform similar actions more proactively to highlight restructuring opportunities to customers, such as those actions needed for the following use cases

  • Proactively notifying the borrower (landlord) of a property to seek remedy in the case of a real estate loan on a tenanted property from a certain company filing for bankruptcy
  • Alerting traders to existing positions that might be susceptible to large mark-to-market moves should certain scenarios manifest, potentially called out in news articles, central bank announcements, etc. 
  • Predicting outcomes and changes of exposure and keeping running scenarios in the background to optimize decision-making on hedging or investments 


To monitor and prevent financial crime and other illicit activities, organizations can use AI to move toward relevant, intelligent monitoring as opposed to static rules. This can save massive amounts of time wasted on false positives, while also minimizing false negatives.

Ongoing KYC and Remediation

AI can find, aggregate, and pull together missing data points on customers from a central repository of all customer data, rather than organizations manually working on a customer-by-customer basis to find all requisite data points. AI can highlight any gaps in data, source these from both internal and external sources, and advise on future courses of action. Using AI, organizations can see huge savings of time, effort, and improvement of customer experience. 

Fraud and AML Monitoring

AI correlates more thorough checks on account activity and payment flows to identify patterns that may point to fraud or money laundering. In a world of instant payments, AI can help monitor flows and check activity patterns in real time, ensuring compliance keeps pace with payment speeds. 

Regulatory Compliance

AI can gather data from within the company on conduct, behavior, selling practices, and more on an ongoing basis and send reports to regulators in the required structure with the required formats automatically. Highlighting any issues or discrepancies, generative AI can calculate a path to resolution in some of these scenarios as well. 

Humans + AI: The Way Forward

The possible use cases for generative AI are many and varied, but this exciting technology is still in its infancy and is far from perfect. It is important to ensure that your organization is not relying solely on AI, but instead using AI as a tool to enhance the work that humans do. 

Some areas wherein generative AI can present challenges include:

  • Ensuring data privacy and security
  • Identifying and correcting hallucinations
  • Preventing bias in training
  • Staying compliant with new regulations in this space

The Instabase Platform enables organizations to automate manual processes and achieve full digital transformation by leveraging the latest innovations in AI. With an open ecosystem that combines AI breakthroughs from research labs and deep industry expertise, Instabase helps institutions embed intelligence into any system or business process to drive transformational outcomes. From financial services and insurance to retail and the public sector, the world’s largest institutions are increasing capacity, reducing costs, and enhancing experiences by automating their business with Instabase.