AI and Machine Learning in Finance: How Bots are Helping the Industry
Khushboo Sharma
Contributor Writer at Hacker Noon| ReadWrite Author |Content strategist | Senior Technical Content Writer | Copywriter | Proofreader | Editor | Specialising in B2B, QA, AI, ML, SaaS and fintech |
originally published at Read and Write
Artificial intelligence and ML are making considerable inroads in finance. They are the critical aspect of various financial applications, including evaluating risks, managing assets, calculating credit scores, and approving loans.
Businesses use AI and ML:
Taking the above points into account, it’s no wonder that companies like Forbes and Venture beat are using AI to predict the cash flow and detect fraud.
In this article, we present the financial domain areas in which AI and ML have a more significant impact. We’ll also discuss why financial companies should care about and implement these technologies.
What is Machine Learning and AI in Finance?
Machine learning is a branch of artificial intelligence that allows learning and improvement without any programming. Simply put, data scientists train the MI model with existing data sets and automatically adjust its parameters to improve the outcome.
Why is AI Necessary For The Payment Industry?
According to Statista, digital payments are expected to show an annual growth rate of 12.77% and grow to 20% by 2026. This vast number of global revenues, done online requires an intelligent fraud system.
Source: Mordor Intelligence
Traditionally, to check the authenticity of users, fraud-detection systems analyze websites through factors like location, merchant ID, the amount spent, etc. However, while this method is appropriate for a few transactions, it would not cope with the increased transactional amount.
And, analyzing the surge of digital payments, businesses can’t rely on traditional fraud-detection methods to process payments. This gives rise to AI-based systems with advanced features.
So, how can AI help the payment industry?
An AI and ML-powered payment gateway will look at various factors to evaluate the risk score. These technologies consider a large volume of data (location of the merchant, time zone, IP address, etc.) to detect unexpected anomalies and verify the authenticity of the customer.
Additionally, the finance industry, through AI, can process transactions in real time, allowing the payment industry to process large transactions with high accuracy and low error rates.
The financial sector, including the banks, trading, and other fintech firms, are using AI to reduce operational costs, improve productivity, enhance users’ experience, and improve security.
Benefits of AI and ML in finance
The benefits of AI and ML revolve around their ability to work with various datasets. So let’s have a quick look at some other ways AI and ML are making roads into this industry:
Better insights with Automation:
Considering how people invest their money in automation, AI significantly impacts the payment landscape. It improves efficiency and helps businesses to rethink and reconstruct their process. For example, businesses can use AI to decrease the credit card processing time, increase automation and seamlessly improve cash flow.
Better-Informed Decision:
You can predict credit, lending, security, trading, baking, and process optimization with AI and machine learning.
Reduced Human Error:
Human error has always been a huge problem; however, with machine learning models, you can reduce human errors compared to humans doing repetitive tasks.
Authenticity and Security:
Incorporating security and ease of use is a challenge that AI can help the payment industry overcome. Merchants and clients want a payment system that is easy to use and authentic.
Until now, the customers have to perform various actions to authenticate themselves to complete a transaction. However, with AI, the payment providers can smooth transactions, and customers have low risk.
Reduced Work Pressure:
AI can efficiently perform high volume; labor-intensive tasks like quickly scrapping data and formatting things. Also, AI-based businesses are focused and efficient; they have minimum operational cost and can be used in the areas like:
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Creating more Value:
AI and machine learning models can generate more value for their customers. For instance:
Improved customer experience: Using bots, financial sectors like banks can eliminate the need to stand in long queues. Payment gateways can automatically reach new customers by gathering their historical data and predicting user behavior. Besides, Ai used in credit scoring helps detect fraud activity.
Use cases of Machine learning and AI in the finance industry:
There are various ways in which machine learning and artificial intelligence are being employed in the finance industry. Some of them are:
Process Automation:
Process automation is one of the most common applications as the technology helps automate manual and repetitive work, thereby increasing productivity.
Moreover, AI and ML can easily access data, follow and recognize patterns and interpret the behavior of customers. This could be used for the customer support system.
Minimizing Debit and Credit Card Frauds:
Machine learning algorithms help detect transactional funds by analyzing various data points that mostly get unnoticed by humans. ML also reduces the number of false rejections and improves the real-time approvals by gauging the client’s behavior on the Internet.
Apart from spotting fraudulent activity, AI-powered technology is used to identify suspicious account behavior and fraudulent activity in real-time. Today, banks already have a monitoring system trained to catch the historical payment data.
Reducing False Card Declines:
Payment transactions declined at checkout can be frustrating for customers, putting huge repercussions on banks and their reputations. Card transactions are declined when the transaction is flagged as fraud, or the payment amount crosses the limit. AI-based systems are used to identify transaction issues.
How AI is changing the online Payment Processing Landscape:
The influx of AI in the financial sector has raised new concerns about its transparency and data security. Companies must be aware of these challenges and follow safeguards measures:
Security and Compliance:
One of the main challenges of AI in finance is the amount of data gathered in confidential and sensitive forms. The correct data partner will give various security options and standards and protect data with the certification and regulations.
Transparency and Trust:
Creating AI models in finance that provide accurate predictions is only successful if they are explained to and understood by the clients. In addition, since customers’ information is used to develop such models, they want to ensure that their personal information is collected, stored, and handled securely.
So, it is essential to maintain transparency and trust in the finance industry to make customers feel safe with their transactions.
Improve Business Models:
Apart from simply implementing AI in the online finance industry, the industry leaders must be able to adapt to the new working models with new operations.
Data Integrations:
Financial institutions often work with substantial unorganized data sets in vertical silos. Also, connecting dozens of data pipeline components and tons of APIS on top of security to leverage a silo is not easy. So, financial institutions need to ensure that their gathered data is appropriately structured.
What’s Next?
AI and ML are undoubtedly the future of the financial sector; the vast volume of processes, transactions, data, and interactions involved with the transaction make them ideal for various applications. By incorporating AI, the finance sector will get vast data-processing capabilities at the best prices, while the clients will enjoy the enhanced customer experience and improved security.
Of course, the power of AI can be realized within transaction banking, which sits on the organization’s usage. Today, AI is very much in progress, but we can remove its challenges by using the technology. Lastly, AI will be the future of finance – you must be ready to embrace its revolution.