Transforming Finance with AI: Step-by-Step Implementation Guide
As per recent statistics, the global AI market will increase at a 20x speed from $100 billion to $2 trillion by 2030. AI is continuously growing and improving in every sector. Use of AI and machine learning in finance is rapidly accelerating.
Another report from 2024 says that around 58% of functions in finance are done using artificial intelligence, previously it was 21%. It clearly shows that financial institutions are incorporating AI into their services at a high speed.
This blog will discuss this shift in detail. Below is what it will cover:
- How AI in finance can benefit a business
- How to Implement AI in Finance
- What are the challenges in adopting AI?
- Application of AI in financial services
- What’s the future of AI in this sector
Let’s go!
What is Role of AI in Finance?
Artificial Intelligence matches human intelligence using technologies like Machine Learning and Deep Learning. It has the ability to analyse vast datasets automatically beyond human capacity which improves efficiency in the financial processes.
A few of its key applications are analysing data, forecasting, high-speed trading, detecting fraud, managing risks, and more. Machine learning plays a crucial role in all of these processes.
Businesses can get real-time insights into finances, customer behaviour, and market trends with a few clicks. No need to search the whole web and waste your time.
AI and machine learning in finance will continue helping businesses automate many time-consuming tasks, improving overall operational speed, thus resulting in better financing.
How to Implement AI in Financial Services?
1. Assess your business needs
Every business has a different process, therefore, their requirements for the tools can differ. Thus, it is important for an organization to assess its needs and know what it expects from a tool. Check the areas where you can implement AI to make the process easier.
2. Select the right AI finance software
Once you have assessed your business needs, it’s time to choose the AI finance software. You must check whether the specific software integrates with your existing tools or not. Go through the web to check feature specifications, interface, scalability, etc.
3. Integrate with existing financial tools
Now, moving to the integration part. Ask your tech team or the software support team to help integrate the chosen tool with the existing ones.
4. Train AI models
The most important step is to train the AI models. It might take several weeks, or it can also be done in a few hours. What should you consider before you train the model?
- Model complexity
- Dataset size
- Computational resources
5. Test and deploy gradually
Before final deployment, you must first test the software at a small-scale level. If you find it working well as per your requirements, you can continue buying its higher versions.
6. Train the workforce
Simultaneously training the AI model, go for training your workforce too. Make them learn the prompts and other aspects of the AI model.
7. Monitor, maintain, and improve
The last and mandatory step is to continuously monitor the functioning of the AI model and improve it if required. This will help you avoid any disruptions between your workflows.
Benefits of AI and Machine Learning in Finance
A full adoption of AI without any manual intervention will take time. But whatever part has been automated so far has brought a lot of benefits, as discussed.
1. High Efficiency with Automation
With the help of AI and machine learning in your workflow, businesses can automate a wide range of financial processes, including:
- Data entry
- Transaction processing
- Generating reports
- Investments and loan management
- Budgeting and forecasting
- Fraud detection and more
This is where machine learning and finance intersect to drive intelligent automation and decision-making.
2. More Accuracy
Sometimes, playing with numbers can result in inaccurate entries, but AI won’t result in. Businesses can expect accuracy in data entry or other automated tasks.
3. High-Speed Data Processing
What takes hours can be done in a few clicks by using AI. By incorporating AI in finance, it becomes really easy to process vast amounts of financial data in a very short time. With automated reports, businesses can speed up analysis and decision-making. It results in faster overall operational efficiency.
4. Lower Operational Costs
As AI helps in automating a lot of tasks, the need for manual intervention decreases. Thus, AI lowers labor costs, making the financial process cheaper.
5. Real-Time Insights
With just a single right prompt, anyone can get insights into market trends, customer behaviour, and other patterns. What’s better than this? You need not go through hundreds of reports for the latest trends.
Examples of AI in Finance
1. AI-Powered Chatbots for Customer Support
Banks and financial institutions use AI chatbots to handle customer inquiries, such as account balances, transaction history, and loan applications. These chatbots provide 24/7 support to your customers.
Examples: Erica by Bank of America and Intuit Assist
2. Automated Bookkeeping and Invoicing
Sending invoices is so easy and fast nowadays with the help of AI. The overall process from categorizing transactions, and processing invoices to reconciliation is done by AI.
Digits is an AI accounting software that offers AI invoicing also. You can get a free demo of this tool on Techjockey.com.
Digits
Starting Price
$ 50.00
3. Credit Scoring and Loan Approvals
AI models analyse alternative data sources like transaction history, spending habits, and even social media activity. It makes loan approvals faster and more efficient. TurnKey Lender is one of the known AI tools for credit decision-making and loan management. Get to know more about Turnkey Lender on techjockey.com.
Turnkey Lender
Starting Price
Price on Request
4. Algorithmic Trading and Portfolio Management
Hedge funds and investment firms use AI for high-frequency trading, risk assessment, and portfolio optimization. AI analyses market trends in real time, executing trades faster than humans.
This is one of the best applications of machine learning in financial markets, where algorithms can identify profitable trading opportunities at a very high speed.
5. AI-Driven Fraud Detection and Compliance
71% of U.S. financial institutions use AI for fraud detection, demonstrating its effectiveness in preventing financial crime.
By analyzing spending patterns, unusual behaviors, and historical data, AI detects fraudulent transactions. Technologists continuously work on making machine learning in banking and finance better to adapt to new fraud.
Challenges in Adopting AI in Finance
Adopting anything new comes with multiple challenges. A few challenges you might face in adopting AI in finance are discussed below. Businesses are advised not to overlook them before finally adopting AI in financial services.
- Higher Initial Costs: Initial set-up costs might be higher for the best tools available in the market.
- Shortage of Skilled Professionals: AI needs to be trained well before you use it. However, there is a shortage of professionals who are highly skilled in training AI models to use the right prompts.
- Risk of Over-Reliance: If you depend on AI too much for every small or big decision without reviewing it, it might lead to errors.
- Algorithmic Biasness: AI models can inherit biases from training data, leading to unfair or inaccurate financial decisions, such as biased credit scoring or loan approvals.
- Security Concerns: If you provide too much sensitive data to AI, cyber attackers can make it their prime target and result in data breaches. This is the biggest challenge everyone is facing with the AI models.
- Regulatory Challenges: Financial regulations keep revolving. Thus, it becomes tough to update AI in real time, but we can expect this in the future. At present, you can overcome this challenge by providing laws and regulations to the model at the very beginning of training them. You can manually update them once any new law is passed.
Automation vs. AI
| Automation in Finance | AI in Finance | |
|---|---|---|
| Functionality | Uses pre-defined rules to execute a task | Uses already-fed data to function |
| Decision-Making | Makes decisions with pre-defined logic and conditional statements | Analyzes patterns to make a decision |
| Human Involvement | Very limited. Requires when new tasks need to be programmed | Minimal prompting is required to make the best use. |
| Examples | Automated financial reports, invoices, payments, etc. | Chatbots, anomaly detection in transactions, credit risk assessment, etc. |
Top AI Tools for Financial Management
Below are a few tools with AI capabilities that can enhance your financial practices. We have provided a brief overview of the tools. You can visit techjockey.com for detailed information about features and pricing.
1. Sage Intacct
Sage Intacct is a cloud-based financial management software that helps in managing financial processes and ERP for a business. A few of the features are:
- Tax management
- Billing and invoicing
- Budgeting and forecasting
- Revenue management
- Financial planning and analysis
Sage Intacct
Starting Price
Price on Request
2. Oracle Cloud Financials
It is another tool that can help you streamline your financial processes. It offers:
- Financial accounting
- Real-time financial insights
- Accounts payable
- Expense management
Oracle Cloud Financials
Starting Price
Price on Request
3. Newgen Finance & Accounting
It is a complete finance and accounting software designed for Startups, SMEs, and large enterprises. What it offers:
- AP & AR
- Bank reconciliation
- Cash flow management
- Financial Accounting and Reporting
Newgen Finance & Accounting
Starting Price
Price on Request
Future of AI in Financial Services
KPMG surveyed around 2300 companies in 2024 and found that 71% of them use AI in their financial processes. This shows that businesses are moving toward AI for faster efficiency.
So, what will AI bring in the future?
Technologies like gen AI and agentic AI will lead the upcoming years. You can imagine that the speed of AI results can increase 10x or more with advancements in machine learning.
It might affect the accuracy or improve it. As of now, there is a low creditworthiness, and we cannot rely on the AI tools, resulting in reviewing the data before submitting it. But, in the future, this might change, and these tools will be updated in real-time so that the data is easily reliable.
As there is no barrier for technologies to expand, no one can tell what new things will come after 50-70 years. Thus, keeping yourself updated about emerging tech becomes really important.
Conclusion
The whole blog can be concluded in just one sentence: AI and machine learning in finance make financial operations faster, and enhancements in AI will also result in the same. Businesses can grow by offering more reliable financial management to their customers.
To thrive in the future, it is advised to stay updated with AI advancements and implement them in your workflows.
FAQs
Will AI replace accountants by 2030?
While AI is making its place in almost every industry, it has fewer chances to replace accountants. With its automation, accountants can make their tasks easier and faster. But replacing humans till 2030 is a tough task for AI.
How can AI be applied to finance?
AI can be used in automating various processes such as analyzing data, resolving customer queries with chatbots, credit scoring, generating automated reports, loan management and more.
Which AI tool is best for accounting and finance?
A few AI tools for accounting and finance are Zeni, QuickBooks, Zoho, and Trullion.
What is the future of AI in finance?
With advancements in AI, we can expect faster results, better accuracy, and real-time data reliability. But you must stay updated on emerging technologies to thrive in the future.
Written by
Mehlika Bathla 
Mehlika Bathla is a passionate content writer who turns complex tech ideas into simple words. For over 4 years in the tech industry, she has crafted helpful content like technical documentation, user guides, UX content, website content, social media copies, and SEO-driven blogs. She is highly skilled in... Read more
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