The Ultimate Guide to Artificial Intelligence (AI) in Finance

Welcome to our in-depth guide on Artificial Intelligence (AI) in finance, a resource designed to offer you comprehensive insights into integrating AI within financial operations.

Get ready to cover essential topics such as:

• Top AI applications for finance teams
• Benefits and risks of adopting AI
• Best practices for leveraging AI to enhance financial operations

Our objective is to equip you with a holistic understanding of AI's role in finance, enabling you to implement AI-driven solutions in your financial strategies with assurance and precision. Now let's get started!

What is artificial intelligence (AI)?

Artificial Intelligence (AI) is a powerful technology that uses computer algorithms and data to mimic human intelligence, make predictions, automate tasks, and provide insights. As McKinsey puts it, "artificial intelligence is a machine's ability to perform cognitive functions that we usually associate with a human mind."

In finance, AI can analyze vast amounts of financial data, detect patterns, and make data-driven decisions. It can help in various areas such as risk management, fraud detection, investment strategies, customer service, and even in optimizing financial operations as a whole.

The aspect of AI that gets substantial notoriety in the media is its ability to perform like a human by demonstrating cognitive functions like curiosity, consciousness, and creativity.

A brief history of AI

Though AI has only recently gone mainstream with ChatGPT, it has existed for many decades. Below, we'll explore 11 key milestones in AI development:

• 1950: Alan Turing proposes the "Turing Test"
• 1955: John McCarthy coins the term "artificial intelligence"
• 1966: Stanford develops 'Shakey,' a general-purpose mobile robot
• 1997: IBM' Deep Blue' defeats world chess champion Garry Kasparov
• 2002: 'Roomba,' the autonomous robot, learns to navigate and clean homes
• 2011: Apple integrates 'Siri' into the iPhone
• 2011: IBM 'Watson' defeats human competition on Jeopardy
• 2014: 'Eugene Goostman,' a chatbot, passes the Turing Test
• 2014-2017: AI consumerization continues with 'Alexa,' 'Tay,' and 'AlphaGo'
• 2017: Google publishes the transformer architecture
• 2022: ChatGPT takes the world by storm

AI's recent evolution

Back in 2017, Google published its transformer architecture. This breakthrough allowed people to train machines with very large data sets.

The more data you have to educate your machine, the better or more specific its outputs will be. This development directly contributed to the rollout of ChatGPT (hence, the T in GPT, which stands for transformer).

What is the core AI model?

Every AI model is based on some sort of data set or training data, which is then fed to a computer that uses a learning model to represent the data.

Here's an example of how finance teams can use AI systems with training data, learning models, and knowledge representation to calculate results for a specific task in the context of credit risk assessment:

1. Training data

Finance teams gather historical data on loan applicants, including their credit scores, income levels, employment history, loan repayment history, and whether they defaulted on loans. This data forms the training dataset, with each entry representing a loan applicant and labeled with their creditworthiness status (e.g., "low risk" or "high risk").

2. Learning model (Machine learning classifier):

The machine learning model is trained using the historical data to learn patterns and relationships between various applicant attributes and their creditworthiness.

3. Knowledge representation:

During the training process, the machine learning model learns to represent knowledge in the form of learned weights and parameters. For example, the model may learn that applicants with higher credit scores and stable employment histories are more likely to be low-risk borrowers.

4. Calculating results:

After training, the AI system is used to assess the credit risk of new loan applicants. When new applicants apply for a loan, their information (e.g., credit score, income, employment history) is fed into the trained machine learning model.

The model applies its learned knowledge to calculate the probability that the applicant falls into the "low risk" or "high risk" category. Based on this probability, finance teams can make informed decisions about whether to approve the loan, set the interest rate, or impose additional conditions.

What is machine learning (ML)?

Machine learning (ML) is a branch of artificial intelligence that leverages algorithms to enable computers to learn from data and make decisions. 

ML is particularly helpful for Accounts Payable (AP) and Accounts Receivable (AR) teams, as it can automate tasks like invoice coding in AP, and make payment predictions in AR. 

This not only streamlines operations but also enhances accuracy and efficiency, allowing finance professionals to focus on more strategic tasks and make data-driven decisions.

What is ChatGPT? 

ChatGPT, developed by OpenAI, is a sophisticated generative AI chatbot capable of understanding and generating human-like text. For finance professionals, it offers a range of applications from generating customer service responses to drafting reports. 

ChatGPT’s ability to process vast amounts of text data makes it ideal for summarizing financial trends and analyzing data, thereby enhancing decision-making and operational efficiency.  It follows the same AI structure as other AI tools, but has been fed an incredible amount of training data, including 570GB of public data encompassing digitized books, webpage content, and social content from Twitter and Reddit. 

Can I use ChatGPT in my finance team?

Finance teams can leverage ChatGPT in several innovative ways, such as automating routine queries, enhancing communication with stakeholders through personalized messages, and improving financial decision-making by summarizing key information from financial documents. 

Get detailed information and examples of how to use ChatGPT to manage your receivables.

What are concerns with ChatGPT?

Every AI model has limitations, and ChatGPT is no exception. Below, you'll find a list of common concerns surrounding the tool:

• No memory: Your interactions with the chatbot don't translate into new learning for the system. If you provide answers to the system, it won't necessarily remember them.  
• Explainability. These models can't always cite sources or explain how they generated an answer. 
• Biases: Every AI model has the potential to be biased.

It's important to note that ChatGPT is built on general knowledge, so it can't answer more nuanced finance questions. Eventually, someone may build out a finance-specific tool, but until then, it's best to take ChatGPT with a grain of salt.

When it comes to using ChatGPT, it's also important to remember that it isn't a replacement for humans. As with all AI tools, you should understand its limitations and how it works before using the system.

AI offers various opportunities for efficiency, accuracy, and competitive edge. 

Finance professionals who understand AI can leverage its capabilities to make better decisions, manage risk, and provide better service to clients and stakeholders. 

Unsurprisingly, some professionals have decided to push AI out of their minds; new things can instill fear, and they have the potential to complicate processes we've become used to. But at the very minimum, finance professionals need to understand AI.

Historic AI applications in finance

If you're wondering whether or not finance is ready for AI, the answer is "yes." Many teams are already using AI today and may not even realize it. For example, here are five existing AI applications in finance:

1. Financial operations

AI and automation have been used to streamline financial operations such as accounting, invoicing, and reconciliation. More specifically, Robotic Process Automation (RPA) systems, a form of AI, have been employed to automate routine and rule-based financial tasks, reducing errors and processing times.

From a more customer-centric standpoint, chatbots and virtual assistants have been used for customer support and answering financial queries.

2. Trading

AI has played a significant role in trading, particularly in high-frequency trading (HFT) and algorithmic trading. Artificial intelligence-driven trading algorithms use real-time data analysis and machine learning to make rapid buy and sell decisions. 

These algorithms can detect market patterns, execute trades, and manage risk more efficiently than traditional trading methods.

3. Risk management

AI has been instrumental in risk management by providing advanced modeling and predictive analytics. In the lending sphere, professionals use AI-enabled credit scoring models to assess the creditworthiness of borrowers.

Further bolstering the risk management capabilities of AI are machine learning algorithms. They analyze market data and historical trends to identify potential risks and optimize investment portfolios.

AI is also used for fraud detection by analyzing transaction patterns and flagging suspicious activities.

4. Document analysis, classification, feature extraction

Finance professionals use AI-based Optical Character Recognition (OCR) systems to digitize and extract information from financial documents like invoices, receipts, and contracts. 

They may also use Natural Language Processing (NLP) techniques for document classification, sentiment analysis, and information extraction. 

5. Benchmarking

AI systems can be used for benchmarking by comparing a company's financial performance against that of industry peers. The machine learning models can analyze financial data to identify key performance indicators (KPIs) and benchmarks for specific industries.

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Here are some of the most important AI benefits for finance teams:

More efficiency gains

AI can automate routine tasks, such as data entry and reconciliation, reducing the need for manual labor and potentially cutting operational costs. This means you can invest your time and money in higher-level tasks.

Time savings

AI automates repetitive tasks, freeing up time for you to focus on more strategic and high-value activities, such as financial analysis and strategy development.

Enhanced decision-making

AI can process vast amounts of data and generate insights quickly. Finance professionals can use AI to make data-driven decisions, which leads to more accurate forecasting, risk assessment, and investment strategies.

Better fraud detection and prevention

AI algorithms can detect unusual patterns in financial transactions and identify potentially fraudulent activities in real time, providing a proactive approach to fraud prevention.  

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Frequently, discussions surrounding AI focus on its potential benefits and transformative capabilities for businesses. But, to comprehensively grasp the essence of AI, it's critical to acknowledge the potential repercussions of not integrating AI into your operations. Here, we will delve into those associated risks.

Wasting unnecessary time on repetitive and manual tasks

Manual tasks often result in inefficient allocation of resources, as your staff spend excessive amounts of time trying to analyze data or route invoices by hand. 

This traditional approach diminishes productivity and hampers your team’s ability to focus on higher-value activities. Leveraging AI's wide-reaching capabilities, finance teams can sidestep the slog of manual number and data crunching. 

Forecasting mistakes

Not adopting AI in financial forecasting could mean your company misses out on complex pattern recognition and real-time data analysis. 

This can lead to less accurate predictions and missed opportunities, putting you behind the curve. In the worst situations, forecasting mistakes can put serious financial strain on your business, resulting in reduced investor confidence, suboptimal allocation of resources, and even potential insolvency in extreme cases.

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The use of Artificial Intelligence (AI) brings several common concerns that span ethical, social, economic, and technical domains:

Privacy concerns

Most AI systems require large datasets to learn and make decisions. This raises concerns about how data is collected, used, and stored, particularly personal and sensitive information, leading to potential privacy violations.

Job displacement

As AI systems become more capable of performing tasks traditionally done by humans, a common concern is that they will replace jobs, leading to unemployment.

Lack of transparency and explainability

Many AI models, particularly deep learning systems, operate as "black boxes," making it difficult to understand how they arrived at a particular decision. This lack of transparency can be problematic in critical applications, especially for finance teams.

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The use of AI in finance teams has sparked widespread concern amongst professionals, with many fearing that AI might render their jobs obsolete. 

However, findings from a recent survey paint a brighter picture, indicating that AI predominantly enables teams to dedicate more time to strategic tasks rather than rendering jobs

The intimidation factor of AI for finance professionals largely stems from its ability to process, analyze, and draw conclusions from data at a speed and accuracy unattainable by humans. 

This efficiency can make AI seem like a competitor rather than a tool. However, the key to coexisting with AI lies in emphasizing the uniquely human skills it cannot replicate, such as creative problem-solving, emotional intelligence, and ethical judgment.

To ensure your role remains indispensable in the era of AI, focus on enhancing skills that are complementary to AI's capabilities. Develop strategic thinking, deepen your understanding of complex financial concepts, and hone your ability to build relationships and communicate effectively. 

Embracing a mindset of continuous learning and adaptability will not only make you more valuable in your current role but also prepare you for the future evolutions of the finance industry.

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Download the full AI report to learn more about AI trends amongst finance leaders. 

Finance teams today are increasingly leveraging AI to enhance their strategic capabilities. Centime's recent webinar discussions shed light on this transition, highlighting how AI is being used to bridge the gap between daily operations and strategic decision-making. 

Here are some key statistics and applications of AI in finance:

• Fraud detection. AI-driven fraud detection enhances security and asset protection, addressing CFOs' need to safeguard financial operations.
• Cash flow forecasting. AI enables more dynamic and accurate financial planning, assisting CFOs in providing valuable insights for growth and stability.
• Budget planning. AI in budget planning supports more precise financial management, aligning with the strategic planning requirements of CFOs.
• Goal setting. AI helps establish more clear and ambitious financial targets, contributing to the strategic guidance CFOs provide to CEOs.

Looking towards the future, finance teams show a strong inclination to further integrate AI into key areas:

• Financial modeling and planning. Financial modeling and planning is a priority for 58% of finance teams who have already adopted AI, indicating a shift towards using AI for more sophisticated and strategic financial analysis.
• Accounts payable (AP). 37% of AI adopters in finance teams already use AI in their AP processes. However, there's a significant interest in expanding AI's role in this area. Some example use cases include invoice data capture and invoice coding.
• Accounts receivable (AR). There are ample opportunities to embrace AI in receivables management. Currently, 36% of AI adopters use AI in their AR processes, and another 44% looking to implement AI within their AR processes, underscoring the potential for AI to further streamline operations beyond what traditional AR automation platforms can provide. 

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AI Expert Leon Gordon recently shared 5 common pitfalls of AI, including falling behind competitors, inefficient processes, inaccurate financial forecasts, limited insights, and inadequate fraud detection and prevention. 

To avoid these mistakes, here are 9 AI best practices to embrace:

1. Define clear objectives. Clearly outline your AI goals for focused and effective implementation.
2. Formulate a strategic vision. Directly integrate AI with financial objectives, targeting beneficial use cases and metrics.
3. Ensure data quality. Emphasize the importance of quality data and governance for dependable AI outcomes and privacy.
4. Implement proof-of-concept. Test AI's feasibility and value in small-scale trials before committing to a widespread implementation.
5. Set ethical guidelines. Craft clear rules for employees on when and how they can use AI. 
6. Adopt an incremental approach. Begin with manageable projects, expanding based on solid ROI and learned insights.
7. Ensure model transparency. Maintain clear AI models for better stakeholder understanding and trust.
8. Strengthen security practices. Invest in robust cybersecurity to protect data and AI integrity.‍
9. Foster continuous learning. Cultivate an adaptive learning culture to stay abreast of AI advancements.