Digital transformation has become a chief focus in almost every industry, and the banking sector is no different. Financial organizations are realizing that by moving away from manual transactions and disparate computer systems, they can gain new efficiencies, attract more customers, and build brand loyalty.
New technology isn’t just making employees’ day-to-day jobs easier within this industry. It’s also expanding banking services companies’ capabilities, helping them work faster and serve more customers without sacrificing data security or quality.
While there are many different digital technologies shaping the landscape, some are more popular than others. Today, we’re sharing a few of the top banking industry technology trends to follow.
6 Banking Industry Technology Trends You Need to Know
1. Generative Artificial Intelligence
With generative artificial intelligence (AI), a machine learns how to identify a digital representation of artifacts from a set of data. Then, it generates new, creative solutions that are similar in scope to the original but do not replicate it.
While this is some interesting, cutting-edge personalization, what does this mean for banking? Financial companies can employ networks that utilize generative AI (known as generative adversarial networks) to create test data for customer-facing initiatives, such as:
- Fraud detection
- Synthetic data generation
- Trade predictions
- Risk factor modeling
Traditionally, it has been challenging to generate data that accurately captures all the possible use cases that can occur. Now, with generative AI, companies can get a full-spectrum look at how these hot-button issues affect all their customers and how to maximize their approach.
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2. Legacy System Modernization
For banks looking to tap into their digital potential, it’s obvious that now is the time to modernize their legacy equipment. As they do so, they’re moving some, if not all, of their services to the cloud.
It’s this emphasis on upgrading their “core” systems that banking institutions are focusing on the most. While investing in new peripheral technologies is helpful, those investments are limited if a bank’s primary infrastructure is old or outdated. By modernizing their legacy core equipment, these companies are extending the lifespan of their systems and extracting more value from them.
3. Autonomic Hardware or Software Systems
An autonomic system is a type of hardware or software system that manages itself. It does so by learning from its environment and modifying its own algorithms in real-time. This allows it to optimize its behavior, even in the most complex ecosystems.
Even without human intervention, these systems can adapt to new environments, requirements, and situations. The technology capabilities are robust and agile, helping banks optimize every aspect of their company’s performance and defend against attacks.
In this industry, most autonomic systems are software-based. However, there are some branches that are already deploying physical roboadvisors to help customers complete in-person transactions. In the future, these robotic assistants will be used to perform a range of services, including:
- Debt management
- Personal finance
- Automated lending
4. Strong Focus on ESG
Customers are demanding transparency more than ever, and they’re holding their banks to the same sustainability standards as their favorite brands. As such, Environmental, Social, and Governance (ESG) strategies are taking center stage.
In short, ESG defines the way a company creates its services or products and the degree to which those methods are eco-friendly, sustainable, and fair.
Today, banks aren’t only concerned with facilitating transactions and managing clients. They’re also tasked with demonstrating their commitment to ESG and helping their customers do the same.
To drive sustainability, companies are deploying fintech ecosystems designed to minimize the carbon footprint associated with traditional transactions. This includes leveraging tools such as:
- Mobile payment platforms
- Digital tokens
In addition, there are tools like ESG-focused roboadvisors designed to persuade users toward smart investments. These robo-platforms use machine learning (ML) to suggest options like sustainable exchange-traded funds (ETFs) that bundle securities into low-risk instruments.
5. Data Protection Through Privacy-Enhancing Computation
According to one survey, 63% of people are generally concerned about sharing their personal data. Moreover, 59% are specifically worried about sharing that data with their bank.
This is where privacy-enhancing computation (PEC) comes in. These systems secure personal data processing in unsecured or untrusted environments. They do so using a variety of different privacy protection techniques.
This way, banks can extract value from customer data without sacrificing their approach to security and compliance. They require this data for a variety of purposes, including analytics, computing, and monetization efforts. By deploying PEC systems, they can securely use customer data to support a variety of activities, including:
- Fraud analysis
- Data sharing
- Intelligence operations
PECs are one part of a larger focus on cybersecurity. Increasingly, banks are taking a “never trust” approach that essentially says they can’t fully trust any user, workload, network, or device. As such, they’re validating every access request through various data points, including:
- User identity
- Device make and model
- Device location
6. A Shift to ML Models
Data analytics have long shaped the financial services landscape. Using tools, like ERP systems with embedded business intelligence, institutions have gained a better understanding of their customer base and created services that cater to them.
With ML technologies, they can take their analytics game to the next level. These systems process data in near-real-time, allowing users to quickly identify patterns, uncover anomalies, create forecasts, and make predictions. Ultimately, this leads to more accurate and autonomous decision-making.
While the benefits of ML are vast, it does require an operational adjustment. To support this new endeavor, banks are embracing MLOps. In short, this means applying DevOps tools to grow their ML initiatives and develop them on a wider scale.
Could Your Banking Company Enhance Operations With Any of These Solutions?
The financial sector is on the cusp of a digital revolution, and the new technologies mentioned above are paving the way.
While these are some of the most pervasive banking industry technology trends, they aren’t the only ones. As companies learn more about ML, AI, and PECs, they’re discovering new ways to apply these tools to their workday. They’re also looking for ways to leverage these systems to promote customer confidence, improve traceability, and optimize the in-branch experience.
If you’re considering digital transformation, contact our enterprise software consultants below for a free consultation.