Trends will matter in the banking industry in the months and years ahead. The challenge is to find out which ones are the most relevant to a given company. Is it big data and AI? Maybe the cloud? Or maybe open banking?
Now is a potential tipping point for banking: the importance of technology has never been as great in financial services as it is now. Strong competition from fintech firms and big tech giants augmented consumer expectations in the industry. Thus, banks and credit unions heavily invest in leveraging new technologies in order to build winning strategies.
We handpicked trends and technologies for bankers to consider this year and in the foreseeable future. These trends are discussed in no particular order of importance.
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Voice Biometrics
The use of biometrics to identify the customer while the person is explaining an issue to a customer service representative on a call. Voice authentication depicts unique characteristics in a person’s vocal pattern and compares them with a prerecorded voiceprint to identify the customer. As a result, the process of verifying a customer’s identity through ID numbers and personal details is ruled out. HSBC, Citi, Barclays, TD, JP Morgan use voice biometrics to establish the identity of the account holder calling the customer services.Eyeprint Biometrics
This authentication permits users to sign in by scanning their eyes with the camera on their gadgets. Eyeprint authentication phases out the need for a password or other security questions, making the sign-in process quick and smooth. Clients of Wells Fargo, Mountain America Credit Union, First Internet Bank can benefit from the biometrics eye print feature.Finger Vein Reader Technology
This technology is used to authorize transactions. Customers just put a finger inside a small desktop scanner instead of entering passwords and PINs. The technology spots unique vein patterns of fingers, which remain mostly the same throughout the lifecycle. Barclays collaborated with tech giant Hitachi to develop a finger vein reader technology for its business customers.
Machine Learning in Banking
Today, banks struggle to keep up with fintech that are usually more nimble than large financial institutions. On the other hand, the big banks have more rooted expertise and much more data. This means, by leveraging the power of AI and machine learning, banks can cut costs, deepen relationships with the customers and more efficiently adhere to regulations.Banks are using machine learning to optimize all areas of their business from risk analysis and fraud detection to marketing, to make data-driven decisions. For example, Bank of America rolled out Erica, its virtual assistant, in 2018. The chatbot applies predictive analytics and cognitive messaging to assist customers 24/7. Erica provides balance information, transfers money to other accounts, views past transactions, and schedules payments.
Cloud-Based Solutions
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Open Banking
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- adoption of open APIs to let external developers build applications
- quicker, easier, and more efficient financial services for consumers
- the application of open source technology to achieve these principles
Data sharing is usually achieved through an API, an intelligent conduit that allows the stream of data between systems. APIs enable the data derived from one software application to be formatted and inserted into the next application without change.
What does this mean for consumers? Open banking enables the account holders to switch much easier from one provider to another to get the best conditions.
Smart Fraud-Fighting Solutions
Cybersecurity challenges have become a day-to-day struggle for banks. Trends show a large surge in hacked and breached data from common sources such as mobile and IoT devices. Even if cyber attacks are not a new thing for banks, the increasing number of cyberattacks have shown that traditional solutions are no longer good enough. Analytics technology is getting better at pinpointing actual high-risk areas and activities. Those banks that succeed in detecting fraudulent transactions or cyber-attacks will also succeed in building strong relationships with their clients. Vendors such as DataVisor, OpenML Engine, Teradata sell fraud detection solutions to banks powered either by machine learning or predictive analytics.
Voice Banking
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Digital Banking
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Financial institutions should develop efficient Startup Relationship Management programs to leverage the technical expertise of fintech startups in order to design digital-only solutions.
To gain a competitive advantage, banks must constantly scout, monitor, and scan their corporate environment to detect risks and opportunities. Innovation Management can be accelerated by running efficient Trend and Technology Management.
For more, check out how the Genossenschaftsverband Bayern e. V. (GVB) - one of the largest German banking unions successfully manages an innovation portfolio with more than 130 banks.