Artificial intelligence (AI), machine learning (ML), and deep neural networks (DNN) are disrupting the business of the financial industry and challenging traditional values.
However, it is certain that AI is quietly influencing the world through a myriad of different applications. AI technology already drives many activities in everyday life, from driving to work to automatically adjusting the thermostat, often without our knowledge. According to Gartner, 40 percent of large enterprises will implement AI solutions by 2020, and more than half will double their existing AI solutions by 2020. This prediction was made before the Covide 19 pandemic exploded, but even taking that into account, the growth of artificial intelligence will still be exponential.
In some areas of industrial AI, machine learning and deep neural networks are finding more applications. One of them is the financial industry, where new technologies are already disrupting business and challenging traditional values.
When it comes to consulting and support, IT companies are able to use AI solutions as efficiently as possible. These can enable companies to leverage the potential of these technologies and improve their processes.
Artificial intelligence plays a crucial role in risk management, and in the financial world, time is money. For risk cases, algorithms can be used to analyze case histories and identify potential problems. This includes the use of machine learning to create accurate models that enable financial professionals to track specific trends and identify potential risks. These models can also be used to ensure that more reliable information is obtained for use in future models.
The use of ML in risk management means that large amounts of data can be efficiently processed in a relatively short time. Structured and unstructured data can also be managed by cognitive data processing. Otherwise, all this would mean a long working day for human teams.
Fraud Prevention
With the significant increase in digital customer transactions in recent years, there is a need to use reliable fraud detection models to protect sensitive data. Artificial intelligence can be used to improve its rule-based models and support human analysts. This in turn can improve efficiency and accuracy and reduce costs.
Artificial intelligence can also be used to review spending history and behavior so that unusual situations can be highlighted, such as a card being used in a short time in different global locations. AI is also able to learn from human corrections and apply decisions based on what should be highlighted.
All fraud management use cases have different requirements for the AI algorithms, and each use case uses them slightly differently. Transaction monitoring requires faster response times, error rates, and accuracy, as well as the availability and quality of training data.
Personalized Banking
In banking, intelligent chat robots based on artificial intelligence can provide comprehensive solutions for customers and reduce the workload in call centers. Voice-operated virtual assistants, often supported by Amazon’s Alexa and with self-learning capabilities, are also becoming increasingly popular. They are able to check balance sheets, control account transactions, and plan payments, and their functionality is increasing daily.
In banking, intelligent chat robots based on artificial intelligence can provide comprehensive solutions for customers and reduce the workload in call centers. Voice-operated virtual assistants, often supported by Amazon’s Alexa and with self-learning capabilities, are also becoming increasingly popular. They are able to check balance sheets, control account transactions, and plan payments, and their functionality is increasing daily.
Many banks now have apps that provide personal financial advice and help in achieving financial goals. These artificial intelligence-driven systems can record income, daily expenses, and spending patterns and then provide financial plans and recommendations. Mobile banking apps can also remind users to pay bills to compete for transactions and interact more easily with their banks.
Quantitative trading
Quantitative, algorithmic, or high-frequency trading and data-driven investments have recently increased in stock markets around the world. Investment firms rely on information technology and data science to accurately predict future patterns in the markets.
The advantage of AI is its ability to observe patterns from past data and predict whether they are likely to repeat in the future. If there are certain anomalies in the data, such as a financial crisis, the AI can examine the data to find possible triggers and then prepare for the future. AI can also personalize investments for specific investors and help them make decisions.
Credit decisions
In many areas, AI is effectively used to better inform the decision-making process. One such area is the credit sector, where AI can quickly and cost-effectively provide an accurate assessment of a potential borrower. Compared to traditional credit scoring systems, AI credit scoring can be much more complex. They can help determine which applicants are more likely to default and which applicants have no reliable credit history.
Models based on artificial intelligence also have the advantage of being objective and unbiased, which can be a factor in human decision making. For many people, good credit is crucial, whether it is to buy an important good, get a job or rent an apartment.
Systems powered by artificial intelligence can become faster, more efficient, and more reliable. These technologies are increasingly used in the financial sector and are being adopted by financial companies on a larger scale. Those who accept the risks that can accompany the introduction of these technologies are often rewarded with streamlined and more productive operations. Artificial intelligence has great potential for the financial world, and business leaders must use the right data to make the most informed decisions.
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