Artificial intelligence and machine learning in banking and retail are not the only technologies that drive these business industries towards digital transformation. However, their versatility and potential are quite promising so their adoption may significantly change the way companies do business. In this article, we will take a look at the ways machine learning and artificial intelligence in banking and retail change these insiders making them digital and data-driven.
What Is Digital Transformation?
According to the Understanding Digital Transformation research, “digital transformation is a process where digital technologies create disruptions triggering strategic responses from organizations that seek to alter their value creation paths while managing the structural changes and organizational barriers that affect the positive and negative outcomes of this process.”
To put it simply, digital transformation is the process of new technology adoption with the aim of making business more effective, optimized, predictable, and data-driven.
What Are the 3 Main Components of Digital Transformation?
There are 3 main components of retail and bank digital transformation.
- customer experience. The adoption of ML and AI in the banking and retail sphere allows for making the customer experience more data-driven and personalized, and thereby, improve the performance of marketing campaigns by carefully tailoring them to the specific needs of each customer.
- operational processes. Machine learning in banking and retail also allows for business process optimization. For example, investment digital transformation allows for making the latter predictable and low-risk.
- business models. Machine learning and artificial intelligence adoption also open the ways for new business model development, for example, digital banks only.
Why Are Banks Going Digital?
There are three core reasons why banks make a high bet on digital transformation with the help of AI and ML.
- Save costs. Artificial intelligence in banking allows for great cost-saving because of better decision-making and business process optimization.
- Optimize the process. Machine learning in banking allows for core business process optimization. For example, dealing with routine tasks is much more effective when automation tools are used, compared to human labor. And this is not the only process in banking that may be improved, optimized, and automated with the help of AI and ML.
- Meet changing customers’ needs. Customer experience change is one of the components of digital transformation. Modern banks understand that going digital and providing financial services according to the needs of a new generation of users is almost the only way to stay competitive, plus get a lot more other advantages.
SPD Group has expertise in digital transformation and implementing AI solutions for banks, so let’s find out how machine learning can be used in the financial industry.
What Is ML in Banking?
Machine learning in banking stands for the smart technology able to work with huge data arrays in real-time and make instant decisions. Thus, the scope of ML in banking applications is quite wide and revenue-promising.
How Is Machine Learning Used in Banking?
Below are the main value-promising ways to use machine learning and artificial intelligence in banking.
- Fraud detection. Credit card fraud detection using machine learning is one of the most promising and effective applications of smart technologies in the financial sphere. Credit card fraud is the main and the most frequently occurring type of identity theft, and the issue of anti-fraud protection becomes even more relevant because of the pandemic-provoked eCommerce rise and the rise in fraudulent activity as well. Machine learning, in turn, may work with huge data sets in real-time, analyzing the behavior of financial services users and making accurate conclusions about the legitimate or fraudulent nature of each transaction.
- Marketing. Financial institutions need competent marketing strategies to promote their services just like any of the companies from other niches. In this case, machine learning and artificial intelligence may be helpful as well. For example, with its help, the process of gathering data about customers’ behavior becomes easier, and the actionable insights obtained after accurate data analysis may be used for marketing strategies, offers, and content personalization.
- Customer service. AI-powered chatbots are the simplest examples of how AI and machine learning in banking may be used for enchanting customer service of financial institutions. However, modern chatbots become smarter each year and may assist customers at every stage of the user’s journey. For example, there are chatbots that help navigate the bank’s website and plan the users’ budget as a part of a banking mobile app functionality.
- Risks predictions. Issuing a loan and making an investment has always been high-risk activities, however, the usage of machine learning and artificial intelligence in banking allows for reducing these risks. For example, smart solutions may be used for automated credit scoring, i.e. evaluating customers’ solvency based on their credit history and the list of assets. Thus, banks may issue loans that have the highest probability of being repaid. The same goes for investment management. With the help of machine learning, investment becomes safer, predictable, and value-driving. In this case, AI systems are capable of making really better and data-driven decisions, since they firstly, know no emotions, and secondly, analyze the combination of the historic and current data to make the most profitable investment decision being guided by logic, mathematics, and probability theory exclusively.
- Robotic assistant. The use of robotics in the financial sphere is very promising as well. For example, AI-powered robots (that may take the form of tiny but computer vision-enabled cameras) may be used to enhance security in the bank branch. In this case, a robotic solution may be used for customer identification, wanted person detection, and making a decision whether each visitor may be involved in money laundering and terrorism financing. There are also solutions that may recognize the emotions and intentions of the visitors, and thus, may be used in the loan issuing process as an additional security measure. Robotic solutions may also be used for customer service improvement. For example, they may assist customers at bank branches with solving standard issues like opening or closing an account. Bank of America already has 22 robots as a part of their digital infrastructure that helps the financial institution with most of its business tasks.
Why Does Retail Go Digital?
Bank digital transformation is not the only goal and sphere machine learning and artificial intelligence may be used in. They can also be successfully integrated into the retail sphere with the aim of making the latter optimized, data-driven, and eco-friendly. Below are the three reasons why retail companies go digital with the help of ML and AI as well.
- Save costs. This is the ultimate goal of any technology adoption, and the usage of ML and AI in retail is no exception. The usage of these technologies across different business processes allows for getting actionable insights on how to reduce the cost of each while increasing the final value.
- Optimize the process. There are a lot of business processes in retail that can be optimized with the help of machine learning – from store’s staff management to supply chain optimization. Each process improvement strives for the previous goal – save costs but increase final value at the same time.
- Meet changing customers’ needs. Modern customers are very picky. The highest-end quality and exceptional customer service aren’t the only thing they want from the retail brands they interact with. They want high-quality content, the feeling that the brand cares about their needs, existing customers’ journey, and entertainment in addition to the purchase itself. What’s more, their preferences and trends they follow change lighting-fast so using machine learning and artificial intelligence is the only way for modern retailers to stay on the same page with their buyers and stay competitive.
How Is Machine Learning Used in Retail?
Machine learning in banking use-cases and the ones for retail are mostly similar since the businesses from these spheres pursue the same goals when adopting ML and AI technologies. So, let’s find out how machine learning and artificial intelligence may boost digital transformation in retail.
- Marketing and personalization. Perhaps when it comes to marketing, there is no one more ingenious than retailers. However, in 2021, ingenuity is far from the key to the success of a marketing strategy, unlike personalization and other data-driven approaches. Retail marketing is no longer possible without machine learning and artificial intelligence since these are the only technologies at the moment that are able not only to process huge flows of constantly changing information but also to extract actionable insights that can be reused in marketing strategies.
- Fraud detection and prevention. The issue of credit card fraud detection is relevant for retail companies as well since they are subjected to online fraud as well as financial institutions. Partially, they both share this risk, and that’s why there are specific AI solutions that provide protection on the side of the merchant and the side of the bank whose card is used to complete the online transaction. What’s more, credit card fraud is not the only retail security threat. Fraudsters are well aware of the fact that online stores have a lot of valuable information about their customers’ identities and their financial data so they consider eCommerce businesses to be the most interesting targets. Thus, eCommerce businesses are also at risk of DDOS attacks (especially during holidays and sales seasons), bad bots, phishing emails, malware, e-skimming, and customer journeys hijacking. Fortunately, ML and AI will also be helpful with all the above issues.
- Supply chain and other processes optimization. Optimizing the retail supply chain with the help of AI is one of the main ways to reduce costs at each of its stages. This is the direct way to reduce fuel costs and food waste since AI may help with competent route planning and demand prediction as well. What’s more, this is not the only process that may be optimized with the help of artificial intelligence and machine learning. For example, AI may help with in-store staff management, and even replace some of them with robotic solutions, which is already a reality in some stores.
- Price prediction. Offering the price the customers would be willing to pay for a certain product is only half the battle since the price and value are equally important for modern buyers. Fortunately, AI and ML for retail may help with both. Data analysis features of AI-powered features may be used for price prediction and optimization taking the market dynamics and business environment into account while researching customers’ moods, preferences, and patterns of behavior may help with the great value proposition development. The only task remaining is to align price with value.
Conclusion
Thus, machine learning and artificial intelligence in banking and retail are some of the most promising technologies for workflow optimization, cost-cutting, improving customer service, and increasing the final value. What’s more, smart solutions become quite affordable even for small businesses, and taking into account the benefits they may promise, they are able not only to quickly pay off but also to start generating profits in the short term.
