In this environment, artificial intelligence and banking transformation are converging elements, as AI for banks facilitates companies in the sector in multiple aspects, such as for calculating risk when granting loans, for offering products and services in high demand or for better management of bank fraud.
Uses of Artificial Intelligence in banking
Artificial intelligence and machine learning are new technologies that financial institutions and banks are betting on to boost their business, automate processes and find new market opportunities.
Let’s take a look at some of the most common uses of artificial intelligence algorithms in the banking sector.
By applying complex and sophisticated AI algorithms, banks can significantly reduce the risk of credit transactions, for example, when a user applies for a mortgage, a loan or access to a credit card.
With the application of AI in this process, in addition to minimizing risks for the financial institution, an ideal solution is also offered to the customer, based on real data about his or her situation and debt capacity.
In addition, the whole process is executed much faster, avoiding the long bureaucratic processes and waiting times that were common practice in the sector when applying for loans and credits.
Artificial Intelligence is used to analyze comments, reviews and posts on social networks and other websites relevant to the bank. The goal is to understand the sentiment and opinion of customers and the public, in general, about the products and services they offer. This can help entities identify areas for improvement and make informed decisions on how to improve customer satisfaction.
This section is directly linked to the previous one. From the moment we know what the customer needs or might need, a huge range of cross-selling possibilities unfolds.
Artificial intelligence is used to personalize offers of banking products and services according to the profile of each customer. For example, the bank can use this engine to analyze transaction history, income and expenses, location and other relevant data to offer each customer products and services that match their needs. This can improve customer satisfaction and the bank’s profitability by increasing sales of additional products and services.
Similarly, it is used to provide personalized financial advice to clients based on their financial objectives, risk profile and other relevant data. A categorization and transactional analytics engine, such as COCO, can analyze large amounts of data to identify investment opportunities and provide accurate and timely recommendations to clients. This can improve customer satisfaction and bank profitability by increasing product sales.
Business process optimization
More internally, transaction categorization can be used to improve efficiency and reduce costs in the entity’s processes.
For example, artificial intelligence is capable of analyzing large amounts of data to optimize asset management, supply chain management and human resource management. This translates into improved efficiency, reduced costs and increased profitability for the bank.
Mobile Apps for banks based on Artificial Intelligence
We can find many examples of artificial intelligence applications in banking apps, for example, to improve customer service with virtual assistants that respond in natural language to customers’ doubts and questions (with full availability or 24/7 services).
One of the major concerns of companies, organizations and individuals in the digital era is cybersecurity, i.e. the concern that their personal and sensitive data is protected (privacy and integrity).
For banking, artificial intelligence is a technology that allows them to increase their level of protection and security in different ways. For example, for customer identity validation, the use of biometric recognition and machine learning raise the level of cybersecurity considerably (use of tokens, one-time keys, verification controls, double authentication…).
The banking sector is one of the most punished by fraud, where cybercriminals are always looking for new ways to deceive users and entities in order to obtain their own benefits to the detriment of both.
The application of AI in the financial and banking sector facilitates the preventive detection of fraudulent patterns, allowing entities to make decisions in real time that enable them to avoid scams or frauds, or minimize their consequences in case they occur.
Challenges for Artificial Intelligence in banking
We have seen different examples of artificial intelligence in banks and how this new technology is changing the way companies in the sector work and proceed.
The implementation of AI confronts banking with a series of challenges, among which we can highlight:
- Intelligent information management. Banks handle a large volume of data on their customers and potential customers. Intelligent analysis of all this information is key to improving their performance and offering products and services that are of real interest to users. Artificial intelligence and big data are key tools for obtaining useful knowledge from all the information that banks handle about their users and the market.
- Complying with regulations and laws. One of the great challenges for companies in the banking sector when applying artificial intelligence to their business model is to comply with the different global laws and regulations on data protection.
- Flexibility in the offer. Until now, the banking sector has been characterized as one of the strictest and most inflexible. Making the necessary evolution to adapt to the current market, using intelligent technology to offer personalized products that are adaptable to the needs of each customer, is one of the great challenges facing the banking industry.
- Ethics and transparency. AI can be very powerful in decision making and process automation, but it can also be very susceptible to the introduction of bias and prejudice. This can be especially problematic in the financial arena, where decisions can have a significant impact on people’s lives. Therefore, it is critical that it is used ethically and transparently in banking. To this end, it is advisable to have an ISO 27001 compliant Fintech partner.
- Lack of skills. The effective implementation and use of this intelligence in banking requires specialized skills that may be difficult to find. Financial institutions may find it difficult to find staff with the right expertise and knowledge, not to mention the increased costs involved. However, there are models of collaboration with Fintech where the partner company is responsible for managing these tools and facilitating the processes.
Customer adaptation and user acquisition in banking with artificial intelligence
Today, the banking sector is facing increasing competition, driven by digitalization and market globalization. Indeed, the constant emergence of new players from all over the world has further increased competition in banking. In this highly competitive environment, banks need to differentiate themselves and attract more customers to survive and grow. As we have seen, Artificial Intelligence (AI) becomes a fundamental technology to achieve this goal.
This mechanics helps banks to attract more customers by adapting to their real needs through the offer of personalized financial products and services. The hyper-personalization offered by certain Fintechs, such as Coinscrap Finance, allows you to differentiate yourself from competitors and improve the customer experience. How does it do this? By enriching transactional data, managing the customer’s portfolio (PFM or Expense Manager), sending recommendations to the user, incorporating automatic micro-services that the customer can customize, etc.
Artificial intelligence and banking transformation go hand in hand today; it is an indispensable technology for the sector to attract new customers and offer products that adapt to the needs of its customers or users.