The financial sector, and banking in particular, is one of the industries with the highest potential applications of AI. Its great capacity to generate data, the volume of transactions and the quantitative nature of its activity, make it a perfect candidate to make the most of the benefits of AI technology, especially machine learning.
How artificial intelligence drives competitiveness in the banking sector
Given how these new technologies can shape new business models and change the traditional client-bank relationship, it is clear that artificial intelligence has started a revolution in the financial sector. It has become a fundamental pillar within the growth strategy of any business, promoting the culture of data and establishing a strategic framework for generating value.
Operational competitiveness is one of the main opportunities we see at everis because it is aimed at offering an experience and a more agile service, which represents a key competitive advantage in the market. Newcomer banking players such as fintech businesses, which specialize in financial technology, as well as big tech companies, have demonstrated the need to reinforce the role of operations in generating outstanding experiences for the client.
AI offers an organizational advantage because it challenges the way that humans and machines interact in a work environment. In order to maximize the organizational benefits of artificial intelligence, banks should provide all major processes of their business with these technologies and train the teams so they can use them to their full potential.
Another opportunity we see is open innovation which implies that due to the intense competition in the market, banks should be open to new forms of innovation that can generate new ways of growing the business. The model imposed by the market is that of “fast curiosity” versus “slow perfection” forcing banks to configure innovation systems that are ultra-connected, agile and decentralized.
Finding new business models is one of the most important reasons why all banking entities should consider implementing artificial intelligence in their systems and processes. AI is a key element to compete in a financial services market where the traditional borders have been blurred. Bigtech and fintech companies are taking advantage of complete freedom and make use of the explosion of new technologies to enter the market and compete in level fields such as financing, Banking-as-a-Service (BaaS) or payment systems, thus eroding business margins of the bank. In fact, BaaS, a new sector within Fintech, continues to grow each year and it even influenced big tech giants to develop their own branded financial services such as several established neobanks with in-house BaaS expertise which have been able to offer their own platforms as partnerships.
A big opportunity lies with diversifying products and services. The unprecedented level of information that banking institutions obtain from their clients, is an unbeatable instrument to design new differential products and services, customized and adapted to demands of the citizens.
Last but not least, we anticipate a big opportunity in hyper-personalized customer experiences, which are tailored customer interactions, an area where AI is essential. A perfect example consists in the algorithms that compare the behavior of millions of people, find similar patterns in consumer preferences thus recommending specific products or services which have a high probability of appealing to the consumer that’s being analyzed.
6. Trends that define the future of AI in banking
There are countless discussions about which trends related to AI technologies will set the pace of innovation in the financial industry. Among the main trends, we believe that the following six are the most promising.
1. Collaborative innovation is the tendency to complement internal resources with the use of multiple external sources, in order to innovate. In this context, there are 2 lines of work that are compatible:
a. Crowdsourcing, a tool to outsource micro-tasks that, carried out at a massive scale, can generate value and accelerate the process of innovation. Among the most popular mechanisms of crowdsourcing we mention open challenges which offer financial entities the option of finding innovative solutions with the help of public platforms, bug bounty which are programs used by credit institutions to detect vulnerabilities in their systems or platforms where small banks pay specialists to create or solve an artificial intelligence related query.
b. Mergers, acquisitions, alliances, entrepreneurship and incubation projects - given the complexity of innovation in today’s world, financial institutions are aware that their internal resources are limited so they seek to incorporate the solutions by forming alliances or merging with other entities.
2. New Machine Learning developments - Machine learning is evolving mainly towards specific developments that have to do with improvements in operational and organizational financial aspects: AutoML and MLOps. The first is a tool to develop models that automate repetitive tasks, and the second one is a set of practices whose objective is to integrate machine learning models, operational development and data, to help manage the life cycle of a machine learning production, which is generally quite complex. Among the main benefits of AutoML are how it automates the creation of ML models without compromising their quality by equalizing their implementation and reducing implementation times and costs.
3. Edge AI - is a computational technique which retrieves, analyzes and processes data very close to the source (for example, a mobile phone). By doing so, entities avoid sending information in centralized centers and they process data in real time. The success of 5G is particularly interesting for this technology because it provides information in real time about the client’s circumstances or characteristics (purchasing habits, hobbies, etc.). Therefore it opens the door to instant recommendations of new financial services based on AI programs. For example, an automated financial assistant could warn a client that a credit card payment would exceed their spending limit, or suggest ways of financing a purchase.
4. AI influencing the strategic design of services - strategic design of services is more and more a catalyst for AI because it generates new opportunities for improvement regarding the customer experience. For example, smart chatbots that provide clients with comprehensive self-help solutions while reducing the call-centers’ workload are developed with AI technology.
5. Contribution to sustainable development - applying AI has a positive impact on sustainable development because it facilitates the launch of new services that favor financial inclusion and education or through data processing to support the investigation.
6.The ethical use of AI is a key challenge when deciding whether to adapt these new technologies mainly because entities such as banks are under public scrutiny for their responsibility in granting loans, in asset management and in how they handle their clients’ confidential data. Therefore it’s important that financial companies become aware of the following challenges:
- explicability which is related to obtaining a reasonable explanation for a certain result.
- discriminating against people.
- systemic risk which might appear if AI would be adopted at a general level.
- advisory obligations which is because there’s a general lack of trust when an AI system has to advise clients.
- anticompetitive conduct - the ability of AI systems to learn autonomously poses a risk of taking anti competitive decisions or that violate the market rules.
Although it presents many challenges, implementing artificial intelligence technologies is vital for any financial institution of the future. It brings a profound transformation which is necessary in order to compete in today’s financial market.
If you want to learn more about how the financial sector can take advantage of AI applications, download this report: "Artificial Intelligence in the Financial Sector"