An integral part of it must be an accurate definition of all the risks associated with the use of dvanced data analytics. The financial services industry is investing heavily in AI and machine learning applications today. They allow you to monetize the collected data, improve customer service, contribute to the development of products and services, and stimulate the development of banks. They can also be helpful in improving operational efficiency. In fact, we are dealing with a significant transformation of modern business strategies, which are increasingly integrated into global economic processes.
Financial institutions that are seriously thinking about how to use artificial intelligence and machine learning for their own purposes must be confident that their internal controls are in step with the growing business opportunities and will be commensurate with all the identified risks that are an integral part of the new situation. …
To make the implementation of artificial intelligence and machine learning effective and safe, iTizzi Software Development Company in Kyiv, Odesa is developing a comprehensive adaptation strategy.
It addresses important issues such as: building stakeholder trust and accountability by properly defining internal controls, including expanding the risk management system, changing/building an operating model, and investing in the acquisition of competencies and talent that support the change process. When building such a strategy, iTizzi in Lviv, Vinnytsia takes into account the regulatory environment and the strategic business goals that you want to achieve. Only such a holistic view will lead us and you to success.
Implementation of artificial intelligence and machine learning in financial markets, on the example of the American market
The US banking sector has made significant investments over the past 10 years to meet increased regulatory requirements following the financial crisis. Banks are actively focusing on development programs that include: business transformation, combating competitive threats, improving customer service and operational efficiency. It is already clear that smart technologies, such as the integrated implementation of artificial intelligence and machine learning, can transform business models and can become one of the key tools that determine the competitiveness of an organization. The banking sector is not only increasing technology investments, but is also considering strategic mergers, acquisitions, and partnerships to scale new investments, which include, but are not limited to chatbots for service, customer communication, cybersecurity risk management, customer behavior, and sentiment analysis for marketing purposes. , scoring models optimization using new data types, service/product recommendations, forecasting. Thus, banks are at a tipping point with technological advances. With rapid innovation and investment, it can be expected that some large banks will be rolling out over 300 different AI and machine learning applications over the next two years. They will increasingly be embedded in user interfaces, product offerings, and operational processes, taking “customer service” to an even higher level.
The adaptation of artificial intelligence and machine learning tools in Eastern Europe is a complex, multi-step process, and a number of elements influence its success. However, it is possible to highlight the main areas of activity, the initiation of which is necessary in order for smart technologies to really start to bring benefits. Preparing for the changes ahead is essential to avoid major disappointments and to make the most of these smart technologies. So, there are four key activities that are inextricably linked to the introduction of artificial intelligence and machine learning in every organization by iTizzi:
- Defining what AI is so that all associated risks can be identified.
- Improvement of existing risk management models, internal control systems to eliminate new types of specific risks associated with artificial intelligence and machine learning.
- Implementation of an operating model that enables responsible use of artificial intelligence and machine learning.
- Build competence by investing in talent that will support artificial intelligence and machine learning.