Did you know that a third of a finance manager’s time is spent on boring, repetitive tasks?
When someone is doing the same thing over and over, complacency and, consequently, human error could set it. Now, imagine what happens when it’s your money that hangs in the balance. Scary, right?
Well, this is the reality that finance professionals face each day. Fortunately, artificial intelligence is about to help the financial sector by taking over the monotonous tasks and allowing executives to shift their attention from transactional to strategic tasks while generating much-needed insights through machine learning.
So, what is machine learning in finance? It’s a branch of artificial intelligence that uses statistical models to allow machines to draw conclusions from large amounts of financial data. The technology is already producing innovations that are nothing short of utopic.
Now, you have virtual finance assistants that are available 24/7 to carry out voice commands. AI-powered software consolidates, verifies, and checks disparate sources of financial data — e.g., accounts, bills, statements, etc. — from a single reference point for the whole organization. Automated invoice systems process multiple orders, handle different formats and supply the missing information to support employees who can lose their focus at work.
AI and ML in Finance: The Real Score
It’s hard not to be impressed. But while AI and ML promise to transform finance through automation and insight generation, certain miscalculations still stand in the way.
Emotions and Contextualization
For one, machines lack emotions, making them unable to contextualize information. Chatbots may be “smart,” but they don’t have the ability to make an empathetic connection with customers.
Process Transparency
The general goal of AI and ML is to teach machines to think and learn on their own, which poses a challenge, especially in the banking sector. Regulations demand that bank processes be transparent and unriddled with unexplained procedures. The General Data Protection Regulation (GDPR), for one, gives European citizens the “right to an explanation” when impacted by an automated decision.
However, decisions derived by AI and complicated algorithms could be unfathomable and unexplainable. Credit approvals, for instance, may not be as straightforward when done by a machine that continuously learns from the gigantic amount of data fed to it.
False Positives
Also, machines are trained to solve specific problems and cannot deviate from them. That may explain the rash of false positives when using ML agents. Such false positives require lots of time-consuming manual rechecking, which defeats the purpose of using AI to speed things up in the first place.
Shortage of Skilled Professionals
The professionals that can put together ML in finance infrastructures are also in short supply. Very few are specializing in the field, and the backlog could run in the thousands. If schools and the financial sector cannot soon provide sufficient incentives to encourage people to tackle this specialty field, AI-inspired reforms could get seriously stalled.
Accountability
Finally, there’s the question of accountability. Who takes the rap when something goes wrong? Do we run after the bank or the robot?
When an accountant, for example, creates an erroneous financial report, businesses have the option to sue him/her. But when the mistake was made by an accounting automation software, can businesses run after its vendor? It’s still a gray area. As financial institutions continue to shift to automated processes, senior managers are urging regulators to clarify expectations from technology vendors.
Applications of Artificial Intelligence and Machine Learning in Finance
Setting aside ethical and technical concerns, developers continue to fine tune AI technologies to fit the financial sector. Let’s examine some of them.
1. Fraud prevention
- Using predictive analytics, algorithms spot fraudulent behavior by examining if an attempted action is characteristic of the cardholder doing it.
- Machine learning algorithms take split seconds to precisely assess a transaction and prevent fraud.
2. Algorithmic trading
- Machine learning helps make proactive trading decisions by monitoring news and trade results to detect patterns in real time.
- Algorithms analyze thousands of data sources simultaneously, helping human traders gain an advantage that can result in significant profits.
3. Underwriting
- Machine learning algorithms perform risk-scoring tasks on thousands of customer profiles, each one having hundreds of data entries.
- The technology helps identify risks and set premiums by making predictions based on historical patterns and current trends.
4. Credit scoring
- Machine learning technology predicts creditworthiness by identifying market trends and news items that can affect a client’s ability to pay.
- Machine learning leverages large amounts of historical consumer data, including those generated by utility companies to produce a credit score for a customer.
5. Robo advisors
- Algorithms and statistics are used to allocate, manage, and optimize a client’s assets based on the risk preferences and desired results.
- Online insurance providers use robo advisors to recommend insurance plans to customers since they charge lower fees and provide personalized recommendations.
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It’s too early to assess the full impact of AI and machine learning in finance where implementation remains to be a costly and complicated process. But as the technology becomes simpler, the risks are calculated to be resolved quickly for the benefit of both the financial industry and its customers.
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