How to Develop AI Tools for Financial Analysts (2023 Guide)

Software Development

5 min read



The booming world of big data has greatly fueled the need for individuals who have a knack for analysis, like data scientists and AI engineers. Now, the demand is exceeding the supply, and companies globally, especially in financial services, are feeling the pinch. They identified the scarcity of data scientists as a key hindrance to their AI ambitions as of 2020. As if that wasn’t enough, 35% also pointed out the problem of lacking robust tech infrastructure.

So, what’s the game plan to tackle these hurdles? Companies need to turn their gaze from basic automation to the sophisticated kind. It’s not about relying exclusively on dated, first-wave automation like RPA anymore. Certain forward-thinking firms are already tapping into the power of advanced AI technologies. 

Another key piece of the puzzle is for CFOs to coordinate with the larger organization on matters related to AI and machine learning technologies. A strategy encompassing the entire company for deploying these technologies can efficiently channel investments and light a spark for better collaboration between finance and other departments.

But it’s not a cakewalk. These technologies are complex, and a few companies have already faced setbacks in their AI pursuits.

If you’re an enterprising business owner steering a finance team and want to create adaptable AI tools, this piece is for you. You’ll learn about the benefits of AI in finance and how you can develop AI-enabled software.

How Is AI Used in Finance?

AI in finance is the integration of artificial intelligence technology into the financial sector. This employs computer algorithms and advanced data analysis techniques to boost and automate financial operations.

Back in 2007-2009, a big financial mess, the Global Financial Crisis, left us all gobsmacked. It pushed the world into a nasty recession. This spurred calls for stronger control over banks.

Let’s see how AI could have spun things differently.

  • House Market Yoyo

In the 2000s, the US house market boomed. Poor credit? No problem. Loans were still available. When the housing bubble popped, many folks found themselves stuck with pricey mortgages. AI could have eyed this yoyo-like market and warned us of the risk in overpriced properties.

  • Risky Financial Tactics

Banks bundled these iffy loans and sold them far and wide. As defaults climbed, the value of these bundles dived, leaving banks and investors in the lurch. AI could have played the canary in the coal mine, suggesting safer strategies.

  • Bank Meltdowns

As losses piled up, banks went under or begged for bailouts. This credit crunch put the brakes on the economy. AI’s ability to flag fishy activities could have alerted regulators early enough to steer clear of a full-blown meltdown.

  • Global Domino Effect

The crisis didn’t stay put in one country. It tumbled through the world economy, leading to a worldwide recession. AI, with its global view, could have sized up the potential worldwide ripple effect, helping institutions brace for impact.

  • Shaky Foundations

Overlying these problems were shaky foundations like high household debt, lax oversight, and a reliance on short-term money. Using AI in finance, the technology could help spotlight these shaky areas and provide policymakers with tips to beef up the economy’s resilience.

While we’re doing better now, banks need to up their game. They still grapple with solid prediction tech, and if they keep missing these risks, losses will continue to pile up. As Statista shows, US commercial banks have taken quite a financial hit.

For Future Teams: Applying AI in Banking and Finance

To upgrade finance departments, we must focus on faster insights, error reduction, and swift decisions. The transformation involves four vital steps:

Shifting Focus Beyond Transactions

Leading companies have boosted efficiency in transactional functions. However, more gains may be limited. In contrast, strategic areas like FP&A and tax planning need efficiency improvements. Innovations like Artificial Intelligence can help, as shown by a manufacturer that cut audit costs using AI. 

Leading the Data Charge in Finance

With data volume and complexity rising, finance teams must navigate this to provide actionable insights. In fact, Mackinsey projects that finance leaders spend 19% more on value-added activities than the typical organization did a decade ago. Of this, 9% is allocated to financial planning, analysis, and business payment.

As such, CFOs and their departments need a master data-management strategy for handling this data surge. They can develop this strategy and apply it to:

  • improving data quality
  • promoting data standards
  • investing in flexible data infrastructures
  • cleansing data
  • and deploying data-quality technologies.

Boosting Decision-making

Advanced analytics are essential for business problem-solving. A company, for example, uses a forecasting tool considering multiple data sources for real-time pricing decisions. Finance departments can provide clearer, faster, and richer insights, driving better decisions. Staff training in analytical techniques can also enhance these insights.

Redesigning the Finance Operating Model

Modern finance departments are adopting flexible models focusing on urgent issues. This demands a new work organization and finances professional. The new model incorporates lean cores, strict data standards, innovative data-management practices, advanced automation, and digital technologies. Implementing these changes will redefine future finance departments.

Functions & Applications AI Tools in Finance

Artificial intelligence tools require key features and functionalities to perform financial analysis. Some of the features and functionalities of AI in corporate finance include:

Data Crunching 

Intelligent software swiftly and accurately processes vast amounts of financial data, collating and examining information from various sources, including market trends, economic indicators, and corporate financials.

Spotting Patterns

One area of AI application is to expertly spot patterns and trends hidden in financial data. This way, you gain crucial insights into potential investment opportunities and market movements.

Risk Evaluation

Sophisticated AI algorithms delve into historical data, market volatility, and other relevant factors to quantify potential risks. Investors can, in turn, mitigate risks and protect their investments.

Predictive Analytics

Leveraging AI’s predictive modeling prowess, the software can anticipate market conditions and investment performance. These projections, based on historical data and patterns, provide investors with an edge.

Real-time Monitoring

Real-time tracking of market changes empowers investors to react promptly to new information and make timely adjustments to their strategies.

Natural Language Processing (NLP)

AI tools can harness NLP capabilities to analyze unstructured data like news articles and social media sentiment. The bottom line of AI’s NLP functionalities is to provide comprehensive insights into market sentiment.

Automation & Efficiency

Applying AI in finance brings significant efficiency by automating mundane tasks like data entry and report generation, freeing up analysts for more strategic endeavors.

Decision Support

Such systems also bolster decision-making by providing data-driven insights, recommendations, and alternative scenarios.

Customization and Adaptability

They’re designed with flexibility in mind and are adaptable to different investment strategies. Also, you can customize them to meet unique user needs and provide a tailored approach to financial analysis.

Planning and Designing AI Tools for Financial Analysis

When planning and developing AI tools, here’s what’s involved in simpler terms:

Identify AI Use & User Needs

Figure out where AI can help in finance. Common AI use cases in finance are spotting fraud, assessing risks, managing portfolios, and forecasting. Understand user needs to make AI financial tools that fit those needs.

Collect & Combine Data

Gather data from different sources for effective financial analysis with AI. Use current financial databases and real-time market data. Mix structured and unstructured data, and bring them together for a full analysis.

Choose & Develop AI Models

Pick the right AI models for the job. These could be regression, classification, clustering, or deep learning models. Make sure you think about their ability to explain things and handle big data and computational needs.

Development Process

  • Pick Your Tech: Choose technologies that fit the project’s goals and your team’s skills. Python, R, and Julia are popular for AI work in finance. Cloud platforms like AWS, Azure, and GCP are handy for handling large amounts of data.
  • Front-end Development: Make user interfaces and visualizations that are easy to use. Use charts, graphs, and tables to make complex data clear. Interactive dashboards let users explore data and get real-time insights.
  • Back-end Development: Create the server-side parts that handle data processing and analysis. Make sure they can handle increasing amounts of data and user requests without slowing down. Always keep data safe.

Train and Fine-tune AI Models

Train AI models on relevant data so they learn the right patterns. After training, fine-tune them to improve their performance on specific tasks.

Training Phase

During the training phase, AI in finance examples and models (like the conversational AI in finance) iteratively process the training data. It makes predictions and compares them to known correct answers. The model’s parameters are adjusted based on the errors made during prediction through a process called backpropagation. This iterative training process continues until the model achieves the desired level of accuracy.

Using relevant and high-quality training data is paramount as it is the foundation for the model’s learning process. If the training data lacks representation or quality, the model may learn incorrect patterns or fail to generalize to new data. 

By utilizing relevant and high-quality training data, the model can better understand the complexities surrounding the use of AI in banking and finance. It thus provides accurate predictions and valuable insights.

Fine-Tuning Phase

After the training phase, fine-tuning comes into play. Fine-tuning involves adjusting the pre-trained model using additional data specific to the financial analysis task. This step allows the model to learn task-specific patterns and nuances, enhancing its performance and AI applications in finance.

Integrate & Test

The use of AI in banking and finance also involves the integration and testing of AI models. Let’s touch on each of them.


Bring in external data and tools to enhance financial analysis. Make sure everything fits together, and data can flow seamlessly.


Check your AI for performance, reliability, and compliance with regulations. Make sure they can handle different scenarios and edge cases to provide reliable results.


In the past, AI’s role in financial automation was rule-based, meaning tasks were processed according to predefined rules. While helpful, these systems lacked the adaptability of modern AI-based automation. They demanded regular human intervention, and updates were slow. In contrast, today’s AI in corporate finance is equipped to manage complex scenarios and entirely automate routine manual tasks.

Nonetheless, AI’s role in finance isn’t confined to automation. It provides professionals with crucial insights to make informed decisions. AI algorithms can process enormous amounts of financial data, identifying patterns, trends, and correlations that might escape human analysts. They handle data with extraordinary speed, allowing for real-time analysis and prompt response to market fluctuations.

Furthermore, coupling machine learning and AI in financial analysis broadens the range of information sources. It can include data from news articles, social media sentiment, and other unstructured data to comprehend the market better and pinpoint hidden opportunities or risks. 

If you’re ready to integrate AI into your business or build new models from scratch, contact us. Our team at Code&Care helps progressive companies leverage the benefits and changing impacts of Artificial Intelligence.

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AI models can achieve high accuracy in financial analysis, but it depends on factors like data quality, model selection, and training. Rigorous testing and validation of AI use in finance are necessary to ensure accuracy. Testing also measures performance against specific benchmarks.

AI tools in financial analysis should integrate relevant and reliable data sources. These include market data feeds, economic indicators, news sources, and internal financial data. The choice of data sources depends on the specific analysis needs and the accuracy and timeliness of the data.

To ensure data privacy and regulatory compliance, it is important to implement robust security measures such as encryption, access controls, and secure transmission protocols. Complying with relevant data protection and financial regulations and conducting regular audits can help maintain data privacy and compliance standards.


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