Top 8 Use Cases Of Machine Learning In Finance
Machine learning has become the core technology transforming every business process.
Table of content
- Smart apps are changing the face of banking and finance
- Banking 4.0 on a smart basis
- The ability to draw reliable conclusions from available data
- Risk assessment using artificial intelligence
- AI identifies potential fraudsters
- How artificial intelligence are used in the financial sector
For ML to be successful, various industry participants such as regulators, office holders and start-ups must work together to build a robust ecosystem in which the potential of ML can be fully realized.
Furthermore, these must enable secure access to data in order to make it easier for ML systems to recognize normal and incorrect behaviour.
Smart apps are changing the face of banking and finance
The digitization of economic life can hardly be thought of and practiced without the megatrends of artificial intelligence and machine learning. This is shown in particular by the triumphant advance that smart technologies have started in the finance departments of companies, but also in the banking and insurance sectors. Reason enough to track down the leap of faith that more and more insiders in the highly sensitive area of finance are granting new technologies and their potential.
Financial institutions have long been investing on a broad basis in innovative AI applications in order to optimize their business models and the customer experience for their customers. The spectrum ranges from reducing operating costs and risks to improving customer support and user experience via conversational banking and chatbots to automatic compliance reporting and AI-based risk modelling in central business areas such as investment management, capital market transactions, lending and payment systems.
Banking 4.0 on a smart basis
Many standard processes in finance are traditionally based on manual and paper-intensive workflows. These lead to susceptibility to errors and increased time requirements – two pain points that AI approaches can easily master. The great advantage of AI systems: You can easily process different data sources at the same time and thus draw valuable conclusions from the enormous amount of data that accumulates in day-to-day business. This is where the need for a powerful cloud infrastructure is essential. It provides an ideal approach to data storage and the processing capabilities required to train new types of AI models. In addition, there is the combination of AI and IoT, through which the financial sector can create products and services.
The ability to draw reliable conclusions from available data
AI is also making its way with pioneering use cases in the finance departments of companies in various industries. Financial management in particular has a great need for reliable forecasts based on available data that can cover an unprecedented spectrum through the use of AI, be it the forecasting and management of bad debts, the real-time evaluation of assets, or compliance regulations relating to spending fraud as well Money laundering. In all of this, smart and adaptive algorithms can provide the CFO and his team with valuable support in order to increase efficiency and optimize the company’s earnings.
Risk assessment using artificial intelligence
Another big field that AI could be revolutionized is risk assessment. This is of enormous importance in many areas of finance. AI systems can use a wide range of information to calculate the risk of default in lending. Algorithms can learn from cases of credit decisions and the repayment behaviour of customers, recognize patterns from them and create profiles. In the case of a new credit request, the system compares the customer’s data, checks for patterns and uses the analysis to classify his creditworthiness. The machine test not only provides knowledge and more security.
AI can also be a powerful tool in market research. With the help of natural language processing, news from all over the world, social media activities and studies can be evaluated in order to make forecasts and identify investment trends at an early stage. With the help of such predictive analytics tools, geopolitical events can be considered and the stability of markets can be estimated. High-performance systems can make this assessment in real time, which holds great potential for high-frequency trading on the stock exchange.
AI identifies potential fraudsters
Know Your Customer programs can use artificial intelligence to screen new customers. The algorithms recognize suspicious patterns and activities and can thus draw attention to money laundering and white-collar crime. The pre-screening of customers is a critical work step for many companies and institutions in the financial sector. A lot of time and manpower is therefore invested in research and testing. Artificial intelligence can examine a massive amount of data in a short time.
One example is claims settlement for insurance companies. AI systems can be trained with data from a large number of insurance cases, especially fraud cases. Conspicuous cases can be marked and presented to employees for further examination, and standard inquiries can be dealt with automatically.
How artificial intelligence are used in the financial sector
- Process optimization through intelligent assistance systems and automated work steps
- Fast and personalized customer advice
- Improvement of offers and services
- Risk assessment for lending or investments
- Early recognition of stock market trends
- Fraud detection and new customer screening
The use cases for AI in the financial industry are diverse and the potential is huge. Once the first hurdles of complexity and cyber security have been overcome, the technology can relieve employees, raise savings potential and create better services for customers. Risks from market fluctuations or attempted fraud can also be better managed through AI. There are also other benefits of AI in the finance sector.
I’m Henny Jones, a Sale And Marketing Manager at HData Systems awarded As Top Big Data Analytics and BI Consultant Company. The company offers services like Data Science, Big Data Analytics, Artificial Intelligence and Data Visualization in Every Industry.