29 December, 2023
Digital transformation is rapidly evolving. Despite the controversial issues surrounding safety and ethics, this year we have seen how AI technologies have impacted not only the way companies operate, but have become a constant presence in many people's careers and lives. Artificial intelligence and data analysis have enormous potential to change business processes and entire sectors of the economy, let's look at what major trends and tendencies we will observe in the very near future.
More and more companies in the financial sector are beginning to use artificial intelligence and data analysis to improve their business processes and make more accurate decisions and increase profits, for example, as in the Otkritie bank case. Let's look at specific examples of business tasks.
Pricing
By offering clients different gradations of interest rates depending on the profile of a potential borrower and scoring, banks rely on developed algorithms and often on "forks" of values that allow different conditions. The most advanced solutions based on AI, trained on high-quality data and the behavior of tens or even hundreds of thousands of customers, allow us to determine the borrower's profile, credit risk, and potential profit of the bank as accurately as possible. At the same time, AI, like a manager, focuses on rules and algorithms, but its expertise is infinitely more extensive, because it learns from huge amounts of data and improves analytics by learning from its work. Similar progress is typical for humans, but still we are not able to process a comparable amount of data and track the path of each client in order to evaluate and refine the profile.
Credit scoring
In credit scoring using traditional methods, experts are limited to a narrow range of financial information, data analysis in turn allows companies and experts to conduct more in-depth research, taking into account a variety of sources when assessing a person's creditworthiness. Here are the main benefits of using AI in credit scoring, reasons why the automation trend will continue to develop in the next year: • More accurate assessment of the borrower’s creditworthiness: the use of AI technologies allows taking into account a greater number of factors. • Lender’s risks reduction: the ability to determine the likelihood of poor service and loan default more accurately. • Faster loan decision-making process: Automation of the credit assessment process reduces the time required to make a decision. • Reduction of credit assessment costs due to process automation.
Fraud prevention
One of the most dynamically developing trends is data analysis for fraud detection. It involves construction of machine learning models, as well as the training of AI systems that help identify suspicious transactions, the use of accounts for illegal transactions or the abuse of loyalty cards and discounts, which saves hundreds of millions for large companies.
Data analysis is already being used to identify anomalies, which means looking for unusual patterns that go beyond the expected behavior. For example, AI modules in accounting and CRM systems can be trained to identify anomalies in mail, user behavior, server logs, and other data sources. It is important to note that fraud detection analysis requires a large amount of data to train models and constant updating to account for new types of fraud. It is also necessary to regularly evaluate algorithms and their performance in order to minimize false alarms and errors. The advantage is that AI learns from these mistakes and will not allow them in the future.
Predictive analytics for investments
One of the main tasks of predictive analytics in investing is to predict prices for stocks, currencies or other financial instruments. Such models predict prices based on historical data, operation of investors, and other indicators such as news or financial indicators of companies. AI systems can be used for a variety of strategies, including long-term investing, short-term trading, or arbitrage. Technologies help to identify potential opportunities for profitable investments and solve complex portfolio optimization problems. The advantages of such systems are that, as in the case of pricing or scoring, they learn themselves in the process and constantly improve their accuracy. With the right approach, such systems learn immediately from their own experience, and from the experience of specialists and experts.
«For growth» trend: robo-advisors
Robo-advisors are automated investment advisory systems that use algorithms and artificial intelligence technology to provide investment recommendations to clients, offering a simple and accessible way to invest based on long-term strategies and portfolio diversification. Reasons for their growing popularity: • Automation and efficiency: Provide automatic portfolio management without the need to visit a financial advisor or broker. • Personalization: Customers can receive personalized recommendations based on their needs and preferences. • Affordability: Fees for using robo-advisors are usually quite lower than those of traditional financial advisors. It should be noted here that AI technologies become trends precisely because they ultimately reduce the cost of the processes in which they are involved, because they already cope better than humans with data processing and self-learning for computational and algorithmic tasks. Plus, they can be loaded with work 24/7, and this makes forecasts and results only more accurate. This raises ethical questions and the role of humans in the labor market in the future, but these questions will in any case face societies over time. As for fatigue and burnout, these same technologies help identify them in employees.
In medicine, AI and data analysis are playing an increasingly important role, and these trends are developing at an increasingly rapid pace.
Using AI for diagnostics
AI is already helping to reduce time and improve diagnosis accuracy by analyzing medical images, data sets and symptoms. Machine learning algorithms and neural networks can be trained on massive amounts of patient data, which helps in identifying patterns and diseases that may not be visible to the human eye. One of the areas where AI is widely used is the analysis of medical scans: machine learning algorithms and neural networks process large volumes of medical scans, such as X—rays, CT, MRI and ultrasound, and identify anomalies and pathologies, allowing for earlier detection of tumors , vascular lesions and other diseases, which in turn improves the chances of successful treatment. In 2024, this development will continue, and the software will be actively implemented in hospitals and diagnostic centers around the world. Another area in which AI shows potential is the analysis of medical data and patient medical history. Algorithms can recognize patterns and relationships between various parameters, which makes it possible to predict the likelihood of disease, the effectiveness of drugs and develop individualized treatment approaches. Here, technology will evolve in tandem with the development of a more personalized, targeted approach to prevention and treatment. In addition to image analysis, AI and data analysis are used to develop innovative prostheses and implants. For example, in the world of nanotechnology, neural interfaces are being developed that allow people with disabilities to control a computer or other devices directly with their thoughts. And briefly about several more promising areas where the development of AI technologies will increase the level of high-tech medicine:
3D printing in medicine
This technology is actively used to create customized implants, orthopedic devices and prostheses: using patient scanning data, medical specialists can create an accurate 3D model of the desired object and print it. This method makes it possible to improve the adaptation and compliance of the device with the patient, and leads to more effective treatment and successful results.
Personalized genetic medicine
Using a patient's genomic data, medical history and laboratory test results, it is possible to create personalized treatment plans, determine optimal methods and predict outcomes for the client, taking into account his unique characteristics and genetic code.
Digitalization and processing of medical documentation
The method allows specialists to analyze medical documents, including medical histories, symptom reports and test results for automatic extraction of information and create a database, which helps doctors and researchers access a large amount of data, reduce recognition time and increase the accuracy of analysis. We have a large case study on this topic together with the N.F. Gamalei Research Institute.
Forecasting epidemics and public health
AI can analyze data on the spread of infectious diseases, blood samples, information from social networks and other sources to predict epidemics and take public health measures. Machine learning algorithms can process large amounts of data and find hidden patterns, which helps timely detection and control of infectious diseases. We also wrote about this in more detail within the framework of the Monitor-Bio project.
Development of new drugs and treatment regimens
AI technologies also make it possible to accelerate the process of discovering new drugs and developing innovative treatment regimens, namely, analyzing millions of compounds and predicting their effectiveness and side effects, which helps reduce the time and cost of research.
Personalized marketing: visualization of the customer's path and buying behavior
Using the analysis of an increasing amount of data and AI, retail companies will offer consumers individually selected and customized product groups based on their preferences and shopping behavior even more precisely than it is now. Information about how customers interact with products and services, adding them to the cart, frequency and duration of viewing, etc. already helps to optimize product placement, better understand customer needs and offer more personalized offers.
The development of video analytics using AI makes it possible to more accurately determine which customers come to the store and at what time, what they prefer and from which categories. Retailers will use this information to adapt the location, appearance, assortment, or even prices and individual discounts on products to improve the shopping experience and customer satisfaction, thus turning the purchase into a unique and individual journey for everyone, which ultimately helps to establish stronger customer relationships, increase loyalty and increase sales.
Supply chain optimization
Data analytics and AI provide more accurate demand forecasts, which helps reduce inventory costs and minimize unsold goods. More efficient logistics and inventory management optimize the supply chain and improve customer satisfaction. This technology will become increasingly widespread and will eventually become the standard.
Customer service improvement
Next year, retail companies will continue to use AI to automate customer support processes and improve the quality of service: virtual assistants and bots using neural networks and natural language processing can quickly answer questions and help customers solve problems. As technology advances, such an experience becomes less distinguishable from live communication. It should be noted that the next step, which seems even more futuristic, is not so far away: in fact, the expected stage is the so—called Internet of things.
Image recognition and video analysis
Computer vision and machine learning technologies will allow retailers to recognize images and analyze videos online to extract valuable information about customer behavior in stores. This method already helps to optimize the placement of goods, as well as prevent theft and ensure safety.
Predictive analysis and modeling
Retail companies will increasingly use AI technologies to create predictive models that will help optimize assortment, pricing, marketing campaigns and operations: such models will help identify new trends and prevent losses.
Energy efficiency improvement and network intellectualization
AI and data analysis are already helping to optimize energy consumption, identify and eliminate energy-wasting processes, and optimize the operation of the energy system in real time. All these processes will continue to increase energy efficiency and reduce the load on the network, significantly offsetting the costs that are needed to develop the technologies themselves.
Equipping power systems with intelligent algorithms, load forecasting, optimizing energy distribution and managing decentralized sources such as wind and solar farms will help manage energy infrastructure more efficiently and reliably.
Expanding the scale of data processing
With the development of the Internet of Things and devices that collect energy consumption data, the analysis and processing of large amounts of data is becoming a prerequisite for making informed decisions in the energy sector. AI and data analysis make it possible to process and analyze this information in order to predict changes and optimize the operation of the energy system.
All these factors together contribute to increasing business competitiveness and improve processes in various industries: digital transformation is not only becoming an integral part of the development of modern organizations, but is also gradually becoming more closely intertwined with all the processes of our lives. Separately, it should be noted the development of geoanalytics and in general the use of geodata for a wide variety of business processes. We will prepare a separate material on this, but we can mention with links to cases the most important areas and tasks that AI systems analyzing geodata are able to solve: manage networks of offices and laboratories, improve drug distribution, assess insurance risks and much more. The development of all these trends, in some areas more active, and in some more noticeable, we will observe in 2024. As a result, forecasts for 2025 should look even more interesting and futuristic.