The intensive introduction of artificial intelligence (AI) technologies into the financial industry has led to changes in the business landscape, reformatting established business processes and business tasks. Today, in the financial sector, AI is mainly implemented in areas such as investing, credit scoring, compliance analysis, market research and customer support. At the same time, the existence of problems and risks associated with the introduction of AI technologies form a trend towards a reduction in the number of medium-sized fintech companies, the consolidation of large players, and an increase in the number of fintech startups with flexible development methodology. In this article, we will analyze the modern practice of using AI technology in the financial sector, as well as identify key problems in the transformation of the financial ecosystem under the influence of this technology.
Today, financial institutions successfully use solutions using artificial intelligence algorithms in lending, payments, marketing, and sales. AI-based systems are being created to detect and prevent fraudulent activities. Insurance companies calculate the cost of their products using complex models. Already familiar chatbots and virtual assistants also function as self-learning algorithms. Thanks to technology, it has become possible to create personalized solutions, taking into account the needs and preferences of each client. Another example is the preparation of individual investment plans, taking into account the planning horizon, risk appetite, and other parameters on investment platforms. But what is causing concern among industry representatives?
Data Quality and Weak Core Structures
The majority of datasets in use are unstructured and from third parties, which makes it challenging for AI and ML systems to detect records that overlap and conflict with each other. Furthermore, the size and scope of AI are not supported by current control systems.
Additionally, if developed by biased creators, algorithm outputs may even display biased findings. For instance, a 2020 article claims that Apple cards give women 20 times less credit since the AI’s judgment was based on a collection of unreliable, historically-biased data. It is obvious that the financial sector lacks a precise moral framework for AI that would guarantee data integrity and fortify underlying data structures.
Lack of standard processes and guidelines
AI for finance sectors are needed now as a clear strategy. The current state of the fundamental structures, which are rigid, unreliable, and fragile, makes it difficult for business and technology teams to work together, leading to outdated operating models. When partnering with or expanding their core tech systems, traditional financial institutions must take into account the context, use case, and type of AI model deployed.
Determining the source of funding is a constant difficulty in AI investing. Will it be an innovation project, an IT project, or a change management project? All three are definitely correct answers, however, just a small portion of budgets are earmarked for AI initiatives.
However, there is some positive news to report. With companies becoming more interested, The Economist’s research team discovered that 86% of financial service executives intend to boost investment in AI over the next five years, with companies in the APAC (90%) and the North American (89%) areas expressing the strongest ambition.
Security and Compliance
The amount of sensitive data that is acquired demands additional security measures, which is one of the primary problems of AI in financial services. The ideal data partner will provide a range of security choices, solid data protection with legislation and certifications, and security standards to guarantee that the information of your clients is handled properly. Seek out data partners who abide with regional or local data laws including SOC2 Type II, HIPAA, GDPR, and CCPA. Additionally, it provides choices for private cloud deployment, on-premises deployment, and SAML-based single sign-on. Secure data access is crucial for PII and PHI.
Tracking Measures of Success
Typically, you may track a few metrics when you release a product, such as usage, engagement, and so forth. However, determining whether an AI program is effective takes time. Since AI results are not correct right away and the system is still learning from new data, how can you tell if the algorithm is changing user behavior for the better or if it has decreased costs or increased efficiency? It becomes tricky to determine if your AI has been “successful” or not, and whether your investment has paid off or not, based on these indicators—which are not impossible to assess, just complex.
Trust in AI
Any new idea needs people to believe in it in order to be adopted and spread. Many businesses, clients, and customers still have doubts about AI and are afraid to employ it, particularly in the banking and fintech industries where so much money is on the line. For instance, the staff in a bank, such as financial advisors, may not trust the solutions provided by an algorithm, which may be justified. Because AI requires a human element as well, being open and honest about the operation of AI algorithms and the types of data that power them may help to build trust. Organizations may transform the workplace by preparing the workforce for additional value-added services and knowledge that can operate in tandem with AI. These are crucial stages in removing unfounded anxieties and, therefore, boosting consumer and employee trust.
AI exploration differs significantly from experimentation with other common programming frameworks/techniques. An algorithm cannot simply be created and expected to function the following day. AI develops knowledge on its own when you offer it more data, context, and domain knowledge for particular use cases. It also learns on its own as you help it iterate with new information. As a result, AI outcomes won’t be precise right away. In actuality, they will be nonsense. If you persist, the algorithm will learn through reinforcement, such as rewards or penalties for decisions that are performed correctly or incorrectly. This process will take a while. Because they have little to lose—and their reputation is unaffected and they have a high tolerance for risk and failure—smaller firms may actually be in a better position to experiment with AI.