AI In The Finance Sector
Artificial intelligence (AI) has brought about a lot of change in recent years. The same goes for the financial sector. Typical goals within the financial sector are to create an advantage and maximize profit. Keeping track of business processes and measuring goals has now been automated. AI also ensures faster and more reliable measurements and results here. In addition, it offers new opportunities.
The manual activities within the financial processes often consist of entering, collecting, and identifying data. Handling financial tasks manually is expensive and time-consuming. In addition, these manual processes can’t withstand an ever-changing environment. When processes are well-defined and consistent, these manual processes lend themselves well to automation.
When we talk about automating manual tasks, we often talk about the use of artificial intelligence (AI). With the advent of AI, computers and machines are able to mimic human intelligence in order to perform human tasks. With the help of so-called machine learning models, AI is able to constantly improve itself. This self-learning aspect in particular offers prospects for many sectors. AI is therefore already widely used within the financial sector and this will only increase in the coming years. In this article, we look at striking examples of practice.
Smarter fraud detection
Smarter fraud detection with cognitive computing capabilities. Traditional fraud detection systems often produce false results. The result is that fraud specialists often spend (too) many hours on research. Cognitive methods such as data mining, machine learning and natural language processors make it possible to analyze more complex and larger volumes of fraudulent transactions. The models recognize risks based on ambiguous or improbable aspects. Another advantage is that these models know how to deal with unstructured data, something that always remains difficult with manual analyses. Since more than 90% of the available data today consists of unstructured data, the implementation of cognitive analytics can give companies a competitive advantage.
The way financial clients receive advice is changing. With the arrival of so-called 'robo-advisors', customers receive online investment advice, for example. The advice that these robo-advisors give is based on investment logic. The outcome? Targeted and detailed investment options based on individual preferences. AI robo-advisors can switch quickly within changing market conditions and individual investment needs. Think of profit, risk appetite, and liquidity aspects. They also monitor and adjust portfolios of individual clients in real-time. Everything, of course, is based on the pre-selected investment strategy.
Artificial intelligence (AI) for better and more accurate credit systems. AI has the ability to completely replace traditional credit scores. Linking credit scores with AI should create more equal opportunities. For example, in today's world, there are still many people who don’t yet have a credit score. In addition, many credit scores are outdated and irrelevant in our current economy. For example, traditional credit scores don’t take into account data such as employment history and financial behavior. As a result, many people have difficulty getting ahead financially. AI can be the changing factor in this. For example, AI can make a good and accurate estimate based on factors such as historical data, financial behavior, and age. This creates a much more accurate risk profile. In addition to creating more equal opportunities, there are other benefits. The benefit to the consumer is at the same time the benefit to lenders; lowering the risk of default. This could lead to lower interest rates and other costs. Furthermore, the advent of AI may enable more people to access traditional financial services. To create a more level playing field.
Customer Lifetime Value (CLV)
The customer is central to every company. For a correct focus, it is important to know what value a customer has. You want to be able to measure that. One metric is central to this: 'Customer Lifetime Value' (CLV). The best possible approximation of how much turnover, added value, costs and therefore profit an individual customer will bring to the company. Various elements play a role in calculating this metric, such as average purchase value, purchase frequency, customer value, and average lifespan. Using machine learning models, the AI system can build on huge amounts of (historical) data to make a good prediction for the future. Previously, almost impossible to do manually, but now an essential management tool and an always up-to-date indicator for determining the value of your company.
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