How Artificial Intelligence is Transforming Decision-Making in the Financial Sector

This article is a continuing series on fields gaining on automation and artificial intelligence. Today’s topic is the Finance Sector.  download article

AI uses powerful technology to transform big data into Financial information. Here’s how:

Illustration of GPU

  • AI relies heavily on Graphics Processing Units,[1] GPUs
    • GPUs are designed to handle massive data streams like video streaming
    • A GPU has hundreds of processors doing different things at the same time
    • GPUs are data processing monsters that run hot
  • Whereas the Central Processing Unit, CPU, is good at doing a lot of random tasks, GPUs are specialized processors that crunch gobs of data
    • GPU has segmented processors that perform many data manipulations in parallel. Then it sorts, merges and summarizes into the needed format.
    • AI uses thousands of GPUs stitched together so it is incredibly fast
    • That is why AI boosts Finance applications- tons of financial data, compiled into reports, incredibly fast
  • The operative word is “generative”, the meaning being able “to generate outputs.”

Generative AI can be used to create financial models, generate investment strategies, and even detect fraud.

Financial Models

Illustration of Financial Model

Financial models are a kind of fill-in-the-blank templates.

Artificial Intelligence applications pull data across a myriad of web servers and feed them to modeling algorithms. The data is compiled into tabular spreadsheet data. Various bar, line, pie charts, and tabular reports can be generated by dropping the tabular data into Excel templates.

Financial models are spreadsheets, usually built in Microsoft Excel, that forecast a business’s financial performance into the future. Typically, this task is performed by a Financial Analyst. With AI, the analyst has a powerful tool at their disposal.

There are several types of financial models:

  1. Three-statement model

A company’s . . .

  1. income statement
  2. balance sheet
  3. cash flow statement

. . . provides a comprehensive view of the company’s financial position and performance.

  1. Discounted cash flow (DCF) model

Estimates the value of an investment by forecasting its future cash flows and discounting them back to their present value using an appropriate discount rate.

  1. Merger model

Analyzes the financial impacts of a merger or acquisition between two companies, including the income statement, balance sheet, and cash flow statement of the combined company.

  1. Initial public offering (IPO) model

An IPO makes a private company public by offering shares of the business for the first time. Allows the company to raise capital from public investors and provides private investors with the opportunity to exit their investment and realize a profit.

  1. Leveraged buyout (LBO) model

Evaluates the target company, determines the amount of debt that can be raised to finance the transaction, and forecasts the potential returns on the private equity firm’s investment

  1. Sum of the parts model

Evaluates a firm by separately assessing the value of each business segment or subsidiary and adding them up to get the total value of the firm

  1. Consolidation model

Combines the financial results of multiple business units into one single model

  1. Budget model

Plans income and expenses over a set time frame

  1. Forecasting model

Estimates or predicts how a business will perform in the future

  1. Option pricing model.

Provides a fair value for an option and helps investors make informed decisions about buying or selling options

 

How AI Generates Investment Strategies in the finance sector

Illustration of Investment Strategies

AI leverages advanced algorithms to analyze large volumes of data to identify patterns and trends that humans may not be able to detect[1][2][3].

Tailored investment strategies can be hatched based on

  • individual risk tolerance
  • investment goals
  • time horizons

This approach allows investors to access personalized portfolios that align with their unique financial objectives[4].

AI can be used to monitor, make, and communicate investment decisions. Asset management firms can benefit from investment reports[2][3].

AI can also be used to improve risk management, fraud detection, and compliance in the finance sector[1][5]. In summary,

AI can generate investment strategies in the finance sector by analyzing large volumes of data, creating tailored investment strategies, and improving risk management, fraud detection, and compliance.

How AI Can Detect Fraud

Illustration of Cyber Fraud

  1. AI can scan data for patterns and anomalies that may indicate fraudulent activity[1][2][3].
  2. Suspicious transactions can be flagged in real-time for further investigation[1][3].

Machine learning algorithms are trained on historical data to identify patterns and trends that typically indicate fraudulent activity[2]. Fraud prevention can be deterred by identifying vulnerabilities in existing systems and processes and recommending improvements[3][4].

In summary, AI can detect fraud by analyzing large volumes of data, identifying patterns and anomalies, and monitoring transactions in real-time.

How AI Helps with Financial Compliance

AI helps financial institutions deal with regulation changes by identifying changes in regulations and recommending changes to existing systems and processes[3]. Compliance risks are identified using the monitoring framework for risk assessment.

AI can help with financial compliance by

  • analyzing large volumes of data
  • identifying patterns and anomalies
  • recommending changes to existing systems and processes
  • and reducing false positives in compliance monitoring.

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