Slowly but surely, AI is quietly impacting the world through numerous and varied applications. AI technology is already powering many everyday activities, from driving us to work to automatically adjusting the thermostat, and often without our knowledge. According to Gartner, 40 percent of major businesses will implement AI solutions in 2020, and more than half will double existing implementations in 2020. This forecast was made before the Covid-19 pandemic hit, but even with this taken into account the rise of AI will be exponential.
In some industries AI, machine learning (ML) and deep neural networking (DNN) have a greater number of applications. One of these is the financial industry, where the new technologies are already disrupting businesses and challenging traditional values.
When it comes to consultancy and support, IT companies like EC-MSP are able to leverage AI solutions in the most effective ways. These can enable businesses to harness the potential of the technologies and enhance their processes.
Artificial intelligence plays a crucial role in managing risk, and in the world of finance, time is money. For risk cases, algorithms can be used to analyze case history and identify any potential problems. This involves the use of machine learning to create precise models that enable financial experts to follow particular trends and notice possible risks. These models can also make sure 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 subject to powerful processing in a shorter space of time. Both structured and unstructured data can also be managed with cognitive computing. All of this would otherwise equate to long hours for human teams to work on.
Kensho is a company based in Massachusetts that provides data analytics and machine intelligence to major financial institutions. Their solutions use cloud computing combined with natural language processing (NLP) to deliver complex analytical solutions in understandable language.
With a massive growth in digital customer transactions in recent years, reliable fraud detection models are required to protect sensitive data. AI can be used to strengthen rule-based models and assist human analysts. This can in turn improve efficiency and accuracy, and reduce costs.
AI can be used to review spending history and behaviors so that it can highlight irregularities, such as a card being used in different global locations within a short space of time. AI is also able to learn from human corrections and apply decisions based on what should be highlighted.
All use cases in fraud management have different requirements for the AI algorithms, but each case uses them slightly differently. Transaction monitoring requires a faster response time, error rates and precision, and training data availability and quality.
Shape security is a company that provides fraud detection services to US banks, and deals with credential stuffing, credit application fraud, gift card tracking and scraping. The organization uses ML models that are trained with billions of requests, so they are able to differentiate between real customers and bots.
In banking, smart chatbots that are powered by AI are able to provide comprehensive solutions for customers and reduce the workload of call centers. Voice-controlled virtual assistants are growing in popularity, and these are often powered by Amazon’s Alexa and have self-learning features. They are able to check balances and account activity and schedule payments, and their capabilities increase every day.
Many banks now have apps that offer personalized financial advice and help in achieving financial goals. These AI-powered systems can keep track of income, regular expenses and spending behaviors, and then provide financial plans and suggestions. Mobile banking apps can also give reminders to pay bills, compete transactions and interact with the bank more conveniently.
Abe AI is a virtual financial assistant which can integrate into various modes of communication, such as Amazon Alexa, Google Home, Facebook, or SMS. It provides services that include support requests, conversational banking and financial management.
Quantitative, algorithmic or high frequency trading, or data-driven investment, has been recently expanding across the world’s stock markets. Investment companies rely on computing and data science to accurately predict future patterns in the market.
Artificial intelligence provides the advantage of being able to observe patterns from past data and make predictions on whether they are likely to repeat in the future. When there are certain anomalies in the data, such as a financial crisis, AI can study the data and notice possible triggers, then prepare for them in the future. AI is also able to personalize investment for particular investors to help in their decisions.
Kavout is a company that uses quantitative analysis and ML for processing data and identifying patterns in financial markets. Their tools are able to process large amounts of data and reduce it to numerical ranks to be applied to particular stocks.
In many fields, AI is effectively used to better inform decision-making processes. One of these areas is credit, for which AI can provide accurate assessments of potential borrowers quickly and at a lower cost. Compared with traditional credit-scoring systems, AI credit scoring can be much more complex. They can help to identify applicants who are more likely to default, and those that lack any reliable credit history.
Models powered by AI also come with the advantage of being objective and unbiased, which may be a factor in human decisions. Having good credit is essential for many individuals, for everything from making large purchases, getting a job or renting an apartment.
AI-powered underwriting solutions are used by companies such as ZestFinance, which enables businesses to assess clients with low levels of credit history. This can provide transparent means of considering groups that would otherwise be deemed high risk.
Systems that are powered by artificial Intelligence can be made faster, more efficient and more reliable. These technologies are finding more applications in the world of finance, and they are being more widely adopted by financial firms. Those that accept the risks that adoption may entail are frequently rewarded by operations that are streamlined and much more productive. AI holds great potential for the world of finance, and business leaders are left to make the smartest decisions with the right data.
Roy Castleman, founder and managing director, EC-MSP