Along with other industries, the banking sector also strives to adopt modern methods and integrate digital technologies into its operation routines. This complex set of measures demands a careful and calculated approach, especially in the field of financial services that involve significant funds and high risks. Let us take a close look at this process and discover why a digital transformation banking strategy is essential for its successful realization.
What is digital transformation in banking
In this context, digital transformation (DT) means multiple changes in the banking industry performed to integrate various fintech solutions in order to automate, optimize, and digitize processes, as well as increase data safety. This process implies multiple large and small changes that reshape the methods and technologies used in the financial area.
Regardless of industry, the primary trend of digital transformation is the integration of computer technologies, and Statista shows us that this trend will only grow.
However, banking has several specific characteristics that define the priorities, goals, methods, driving forces, and other features of the DT peculiar to the financial area.
Digital transformation in investment banking
Investment banking deals with the business sector and big sums of money that sometimes can transform into even greater losses for a bank or another financial institution. In fact, without smart digital transformation, the investment banking is doomed to fail because of the growing complexity of fraudulent schemes and equally growing competition on the Fintech market.
Fraud detection of startups
According to StartupBlink, there are over 52 420 startups worldwide. Without proper fraud detection software, banks can never know whether they’re looking at a promising startup that will turn into a unicorn or another fraud that will disappear right after they will get the investment money. Banking institutions invest in their own custom fraud detection systems, and the ones that already use them have vastly reduced or even fully eliminated the risk of giving loans to frauds. These systems include Artificial Intelligence or Machine Learning elements, and the precision of their results fully depends on the quality of calculating modules and software implementation.
Big Data is used by the investment banking institutions mainly for fraud detection, forecasting, and analytics. Big Data, in combination with Machine Learning, can protect your financial institution today by detecting frauds, customizing offers for every client, and increasing the safety of transactions. This information also helps in building and adjusting a customer journey map to improve the satisfaction and retention of clients. Besides, this combination can protect your Fintech business tomorrow by giving predictions on future changes. Thus, you can be more careful with partnerships, loaning decisions, staff hiring, etc.
Market modeling and analysis
Analytics software is crucial for all companies in the banking sector. The digital transformation that is actively happening in all industries changes the conditions for the banking sector as well. A good-quality analytical platform can present you forecasts for the next few years, months, and even decades, and give you an opportunity to adjust your business strategy if needed or show you that you’ve already chosen the right development direction and you should stick to it. Depending on the type of your software and input information, you can get multiple future market modeling scenarios.
Digital transformation in retail banking
Unlike investment banking, retail banking deals with the B2C sector and has its own difficulties and peculiarities that can be easily solved with a proper combination of software and hardware.
Fraud detection of individuals
When a manager checks a loaning request, it may take from hours to days, and there is no guarantee of missing out some vital information, which results in a bad decision for the banking institution. However, banks that have integrated KYC (Know Your Customer) software get the validation process done within minutes and make decisions of significantly better quality. Depending on the access to official databases, you’ll be able to check and validate clients on their administrative and credit history and optimize your loaning and other financial processes. In some cases, the information scraped from open profiles in social media can also help to identify frauds.
Retail banking deals with thousands of clients and their transactions on a daily basis. Big Data solutions will help you to enhance your capabilities and increase client satisfaction and retention rate. Clients expect their payments and all kinds of requests to be processed immediately. When the system makes them wait for minutes, it is a bad customer experience resulting in your clients switching to services of your competitor.
Internet of Things
Internet of things integration as a part of your digital transformation will make processes related to customer service as optimized as they can ever be. For example, a customer tracking system will collect data on the movement of your customers and staff, process it, and show areas that need improvements or rearrangement to increase the quality of services. IoT also helps to customize offers and start mutually beneficial partnerships with companies engaged in other industries. For example, your IoT system can track that your bank client browses a specific car model and at least once visited a car dealership. After the KYC system confirms that this client has a good history, you can send the client a customized offer in the form of advertising, telling about your “new” car loaning program.
Advantages and disadvantages of digitization in banking
Digital transformation offers the following benefits to financial institutions:
- Improved security on all levels of data handling. Data encryption save banks from external and internal leaks of information to frauds and competitors. Most importantly, it increases the safety of transactions.
- Faster operation and lower waiting times. Clients don’t like to wait, especially when they trusted your bank with big sums of money. Big Data processing systems with microservice-based architecture ensure fast and safe transaction processing.
- Better analysis and risk management for banking operations. You won’t have problems with fraud schemes if you have good fraud detection systems. Also, multi-level validation of transactions will eliminate the possible mistakes of your customers and your staff.
- Predictive capabilities. Knowing in advance what problems and transformations your market will face in the future is a key to your financial success. Having trusted information on different possible scenarios from minor stirrups to a global economy crisis will help you to prepare in advance. This way, you can make the right business decisions and integrate winning Fintech solutions before your competitors or switch your business to another more promising and financially rewarding industry.
- Customization. Customers hate receiving standard offers they don’t need but are positive about receiving timely offers aimed at solving their particular need. Software with the right analytical and data mining and processing compounds, you will be able to customize your offers and make this process automated and safe.
- Automation of repetitive tasks. When managers extract same information to build same reports over and over again, it is mindless and inefficient labor for your staff and your company because you’re paying salaries for something that is done by the people for hours or days and can be done even better by one piece of software within seconds.
However, these benefits have their price, so here are some drawbacks of digital transformation in the banking industry.
- High risks in case of poor implementation. Banking is one of the few business areas that involve risks of extremely large financial and reputational losses. That’s why all digital transformation initiatives in this area must be carefully planned, modeled, and tested. The main goal here is to prevent any disruption of the existing flow of business processes and integrate innovations as seamlessly and safely as possible. Otherwise, vulnerabilities may appear that lead to leaks or loss of confidential information or a chance of unauthorized access to bank accounts.
- High requirements for hardware and personnel. Digital technologies need highly skilled specialists to implement them in the most effective way. Moreover, hardware and software become obsolete over time, and businesses have to replace legacy systems with modern alternatives. For banking, the stakes and the requirements are much higher than in many other industries.
- High costs. With such high requirements, banking demands cutting-edge technologies and best specialists available on the market. As a result, significant costs are involved. However, high costs on top-quality software result in bigger income and safety.
Examples of Software Solutions for Banking
Banking institutions will benefit from implementing the following solutions in terms of their digital transformation strategy:
- Fraud detection system
- Know Your Customer software
- Big Data analytics platform
- Data Encryption
- Big Data mining and processing software with a microservice-based architecture
- Modeling and simulation software (for predictive analytics needs)
- Data generation solutions (banks don’t share their information with other financial institutions; thus, banks face problems with getting enough data for machine learning purposes like creating fraud detection systems)
- Virtual assistants
- Online banking applications
Complex solutions may be costly, but when it comes to banking, there’s no room for a budget decrease in custom software development. The biggest banking market representatives have started their digital transformation years ago and now openly tell about their success.
For example, Bank of America indicates that by the end of Q2 of 2020, their AI-driven virtual assistant Erica completed 160 million client requests and reached 14.4 million total users.
Modern banking is mostly associated with Big Data. For this reason, most technologies that are actively implemented during the digital transformation of the financial sector are highly beneficial for collecting, processing, storing, and analyzing large amounts of information. Here are 4 most popular types of computer technologies in banking:
- Artificial Intelligence and Machine Learning. AI and ML are powerful technologies, even when they are used separately. However, their synergy multiplies their effectiveness if they are used in combination.
- Blockchain. While this technology is mostly associated with cryptocurrency, it is used in banking for its extremely high security features. It ensures safe storage of data and protects it from tampering.
- Cloud Computing. In addition to personal data centers and warehouses, financial institutions also use a wide range of cloud-based services.
- Internet of Things. Smart devices are the best way for enterprises to learn about their current and potential clients, find out their needs and desires. Banks use personal information to create customer journey maps. They also use it to develop and adjust their marketing strategies using targeted advertisements. Personal information collected using the IoT is also used as a source for further analysis performed with Machine Learning to find customer behavior patterns and train Artificial Intelligence to recognize subtle signs of fraud.
As in most industries, digital transformation in banking is a very expensive set of measures. A financial institution must consider this fact and prepare the full amount of resources required for successful implementation.
If you want to maximize the benefits from digital transformation, you need a comprehensive strategy and skilled specialists. Contact us, and we will perform all stages of integrating digital technologies into your enterprise. We understand the high requirements in this industry and have the skills, experience, and discipline to provide top-quality realization and flawless results.