AI in Finance
8
Apr

AI in Finance: Is Security a Concern?


Global technology has evolved over the years and nowadays, humans are gradually adopting artificial intelligence. Artificial intelligence comprises a lot of things that range from the process automation of robotics to the actual process of robotics. Furthermore, artificial intelligence and machine learning are getting popular among large organizations due to the amount of data they deal in.

Nowadays, artificial intelligence in the financial sector is among the technology that is paying a lot of dividends. Furthermore, AI in financial services has helped the banking world and financial industry meet the demands of customers who want smarter, convenient, and safer ways to access, save, spend, and invest their money.

This shows why thousands of companies worldwide see the benefits of AI for financial services and are adopting it. This article will highlight everything you need to know about conversational AI for finance and the potential benefits of AI for finance.

Applications of AI in Financial Services

Currently, AI algorithms are used by financial institutions across every financial service while focusing on key business benefits and pressure by tech-savvy consumers. Some of the applications of AI in finance are stated below.

AI in Personal Finance

One of the reasons behind the adoption of artificial learning in personal finance is that it can manage one’s financial health. AI in financial services offers 24/7 financial guidance through chatbots or to personalize insights for wealth management solutions. Furthermore, this makes artificial intelligence and machine learning an important factor for any financial institution that wants to be a top player.

AI in Consumer Finance

Consumers are on the lookout for banks and other financial services that can prevent fraud and cyberattacks because of the increase in online payment fraud losses. One of the important aspects of artificial intelligence is that it helps to prevent fraud and cyberattacks. This is because artificial intelligence can analyze and discover irregularities in patterns that humans may not find.

AI in Corporate Finance

Artificial intelligence solutions are also important in corporate finance. This is because machine intelligence can better assess and predict loan risks. With AI technologies like machine learning, organizations that want to increase their value can reduce financial risk and even improve loan underwriting.

Furthermore, AI can reduce financial crime by spotting anomalous activity and advanced fraud detection while the company analysts, investors, accountants, treasurers can then work toward long-term growth.

AI and Credit Decisions

With artificial intelligence, credit decision-making becomes faster, and a more accurate assessment of the potential borrower can easily be determined at less cost. Furthermore, credit score with AI uses more complex and sophisticated rules when compared with traditional credit scoring systems.

This helps lenders differentiate between high default risk applicants and those credit-worthy but do not have an extensive credit history. Furthermore, a benefit of AI in credit decisions is that a machine is not likely to be biased like a human.

Some of the other application of Artificial intelligence in finance and banking are

  • AI and Risk Management
  • AI and Fraud Prevention
  • AI and Trading
  • AI and Process Automation

Benefits of AI in Finance

There is a huge benefit to implementing AI for finance, such as fraud detection, task automation, and delivering personalized recommendations. Some of the benefits of AI for financial services are stated below.

  • It reduces false positives and human error
  • It enables frictionless and 24/7 customer interactions
  • It helps in saving money
  • It reduces the need for repetitive work.

The Challenges of Artificial Intelligence

Although there is a huge benefit of Artificial intelligence, it can be quite challenging in the real world to reap the benefits from AI algorithms. Some of the challenges of artificial intelligence are stated below.

Data Quality

It is a known fact that an algorithm prediction power is dependent on the data quality fed as an input. Even if the data are gotten from quality sources, there are usually biases hidden in the data. Furthermore, the effort and time needed to gather an appropriate data set cannot be underestimated.

However, there are problems in the reconciliation of data from front to back in the financial industry, and most data referential do have quality issues. This shows there is a need for a data-quality program put in place before any large-scale artificial intelligence initiative.

Black-Box Effect

Most results from algorithm intelligence are usually not verifiable. Furthermore, machine intelligence gives statistical truths, which means they can wrong in some cases. There may also be hidden bias in the result that can be hard to identify. However, diagnosing and correcting the algorithm can also be very hard.

Machine learning can be quite disturbing to a banker’s rational mind because there is no reason why the algorithm provided a negative or positive result to a question. This is the reason why the use of AI in trading is limited.

Narrow Focus

Intelligent algorithms are designed to solve specific problems, and they cannot deviate from that. For instance, an algorithm designed to detect suspicious payments cannot detect other suspicious activity related to trading. Furthermore, algorithms lack emotional intelligence and the ability to contextualize information which is why banking chatbots usually disappoint.

Responsibility

When it comes to liability, machine learning’s major challenge is who will be held responsible when something goes wrong. This is why most financial institutions are afraid to give machines full autonomy as they cannot foresee their behavior. Furthermore, financial institutions usually have a human supervisor that will help validate the machine’s decision for critical activities, defeating the reason for using the machine in the first place.

AI For Finance Course

Artificial intelligence is among the top digital transformation strategy in finance today. Furthermore, it can be used for practically everything in the digital world. This is why IT experts and any practitioners need this course. In this course, you will learn how to apply SOTA ML techniques for solving financial problems.

You will also learn how to execute complex financial calculations done in the finance industry with advanced methodologies. At the end of the course, you will know everything about using python-based programming for different sectors in the financial industry and doing efficient and effective data analysis.

Visit CodeRed to learn more: https://codered.eccouncil.org/

References:

  1. https://marutitech.com/ways-ai-transforming-finance/
  2. https://builtin.com/artificial-intelligence/ai-finance-banking-applications-companies
  3. https://www.businessinsider.com/ai-in-finance?IR=T
  4. https://towardsdatascience.com/the-growing-impact-of-ai-in-financial-services-six-examples-da386c0301b2
  5. https://internationalbanker.com/finance/the-impacts-and-challenges-of-artificial-intelligence-in-finance/
  6. https://hub.packtpub.com/how-will-ai-impact-job-roles-in-cybersecurity/

FAQs

Will AI take over cybersecurity?
Although AI-driven systems have started to replace humans in numerous industries, this is not the case in cybersecurity. Using AI to spot cyberattacks is not that practical as such systems will produce a wide range of false positives. Furthermore, relying completely on artificial intelligence to manage security will lead to more weaknesses and attacks. This shows that automation can only be used to support cybersecurity professionals but not to replace them.
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