How is AI used in financial analysis?

In the last few years, there has been a debate on AI’s impact on all aspects of business, including finance and banking. AI has been reshaping operations and streamlining processes across the entire financial services industry. Thanks to automation, more and more companies are able to reduce costs and prevent human error. It’s no different with financial analysis – Artificial Intelligence analyzes vast amounts of data, covering insights that would be impossible for humans to detect on their own. 

AI in banking and finance – financial modeling explained

Let’s start with a short explanation of what financial modeling is. Simply speaking, it’s the process of creating a mathematical representation of a financial situation using numerous financial data and variables. Financial modeling plays a crucial role in forecasting, planning, and decision-making. It allows lenders to assess the creditworthiness of applicants, determine the terms of loans, as well as manage credit risk. 

Recently, financial modeling has been taken to new heights with AI precision. Artificial Intelligence has addressed several challenges in traditional financial modeling, offering accuracy and efficiency. It:

• Processes structured and unstructured data from various sources to quickly identify patterns and trends.
• Automates data gathering, cleansing, and entry which reduces the risk of errors.
• Accelerates forecasting and analysis, making rapid and accurate predictions.
• Analyzes data in real-time which makes it possible to respond instantly to changing financial conditions, assess risks, and seize opportunities. 
  Supports companies in risk management, detecting anomalies, and providing early warnings.
• Automates compliance tasks, reducing the risk of regulatory violations.
• Provides personalized investment insights and portfolio management based on individual financial situations and risk profiles.

Large Language Models in credit scoring

AI-boosted credit decisioning is a modern approach to assessing an applicant’s financial health. It uses Machine Learning algorithms to analyze various data points to predict an applicant’s likelihood to pay a loan. Such technology provides lending companies with a more accurate and nuanced understanding of an applicant’s financial behavior. 

What does the process look like step by step?

1. The data from various sources is collected. It’s gathered from various sources, including banks so that it can provide a more holistic view of an applicant’s financial habits.

2. Machine Learning models are trained on large datasets to identify patterns and correlations that might indicate an applicant’s likelihood of repaying a loan. 

3. Once the ML model is trained, it can be used for predictive analysis – a tool such as AI Assistant evaluates the applicant’s data against the patterns learned during training. Then, it generates the credit score that predicts the creditworthiness of the applicant. 

AI-powered models, unlike traditional methods, also focus on the past performance of an applicant. Therefore, they are more sensitive to real-time indicators such as current level of income, employment opportunities, and potential ability to learn. AI models support lenders in two main areas:

• Automated credit review.
• Automated credit extension.

Large Financial Models in the lending industry 

LLMs are models used for performing NLP (Natural Language Processing) tasks. One of the most famous ones is obviously ChatGPT which has become common among various sectors including banking and finance. Some companies, such as Planky, train and fine-tune LLMs for specific purposes and business requirements to make sure that AI technologies are perfectly suited to their needs. 

PlankyGPT – lending processes moved to a brand-new level

PlankyGPT  is a comprehensive, powerful platform designed with lending businesses in mind. It helps lenders get a holistic overview of their customers’ financial conditions, as well as understand their spending behavior better. It makes credit decision-making easy and automated, based on real-time transactional data. Therefore, it allows companies to create financial products that are better adjusted to their customers’ needs. PlankyGPT AI Assistant is powered by Open Source LMM Llama3.1. Large Language Models are only trained on the client’s data, no one else can access and modify them.  

What’s in for you?

• You can gather all financial and transactional information of a particular customer or a group of customers in seconds.
• Your credit decisioning processes become more accurate and flexible.
• Credit underwriting is more efficient also for applicants – they don’t have to deliver tons of paper documents and they get a credit decision instantly.
• You can make credit decisions based on additional metrics for better risk management. On top of traditional scoring data, AI Assistant also delivers bank statements, employment data, utility payments, shopping behavior information, rent payment data, etc.
• The tool can be easily integrated with your existing systems. No coding knowledge is required.

How PlankyGPT AI Assistant works?

The tool is simple and extremely easy to work with. All you need to do is ask the right questions. You may ask your AI Assistant to browse datasets and find the one you need or to create a new dashboard with the data you need to make a decision. The tool instantly delivers the report you need, without time spent on searching. You can use standard and non-standard metrics to discover even more helpful insights. The financial data is available on transaction, customer, and portfolio levels.

Takeaway

There are many ways to use LLMs to increase the efficiency of credit decisioning and streamline loan operations. The most important thing is to collaborate with an experienced partner who knows how to properly use the models. If you want to get familiar with a tool that has revolutionized the way dozens of our clients work, we’ll be happy to show you how to embrace LLM to gain a competitive edge.  

Ready to unlock new avenues of excellence in delivering financial services? Book a demo!