How evolving tech is changing digital habits
Today, I can ask my digital voice assistant to tell me the weather forecast while getting ready for work, set my smart thermostat to adjust when I leave home, and listen to a playlist curated by Spotify on the train – all before arriving at the office.
McKinsey says AI could potentially deliver up to $1 trillion of additional value each year for global banking
Thanks to new technologies that know us better than almost anyone else, it can make our day-to-day more personalised, simple, and streamlined.
It’s no wonder then that we as consumers have started to ask for the same level of customer service from companies that we do business with daily, including banks. In fact, these days any digital native would scoff at the prospect of having to go into a bank branch and fill out a long form by hand to open an account.
Changes in consumer habits due to digital banking continue to be a strong topic of interest for any financial services institution. Banks have come a long way from the traditional way of doing things and technology has replaced many manual processes.
But many business practices have not changed, and current processes are set to be replaced, with artificial intelligence (AI) continuously evolving and taking things one step further.
McKinsey says banks must become “AI-first” to survive and estimates that AI technologies could potentially deliver up to $1 trillion of additional value each year for global banking.
Many technologies that have advanced the way we bank, like data capture or optical character recognition (OCR), have been around for a long time. More sophisticated tools are changing everything from lending, customer onboarding, account opening, customer service, risk and compliance, security, and anti-money laundering, as well as transforming back-office technologies to ensure smooth 24/7 banking.
Let’s take a deeper look at a few technologies available for banks to use today that are changing digital habits and the way we bank.
Low/no-code tools
One of the major challenges established banks are facing today is how to keep up with the power and speed of fintechs. Previously, banks had a team of developers and professional coders dedicated to building a mobile app or banking system, but this is starting to change.
Low/no-code platforms allow organisations to utilise fast-maturing technology to experiment with and deploy at scale, helping them react quicker and match the disruption caused by fintechs. The projected growth in market size for low/no-code is expected to reach $45.5 billion by 2025, according to MarketsandMarkets.
The low-code approach enables bank business staff, or citizen developers, to build applications and systems with easy drag-and-drop components. The no-code approach makes it easier for everyday business users to quickly design, train, and deploy software robots with skills that can understand content, such as the diverse set of banking documents associated with lending, without having to be a machine learning expert or relying on the IT team.
Process intelligence
Thriving in our new digital age means optimising processes and content to enhance compliance, streamline workflows, and revitalise the customer experience.
Before the surge of digital changes, banks used to evaluate branch performance during quarterly reviews then make necessary changes six months later. Now, process intelligence can send alerts in real-time, improving performance on the spot and preventing any delay with customer experiences and outcomes.
Today’s systems can generate large amounts of data from both digital and physical sources. When it’s consumed and analysed properly, this wealth of data can be used to discover patterns and insights that lead to better customer experiences, new operational efficiencies, and increased growth and profitability.
Content intelligence
Content intelligence technology helps the digital workforce – or software robots – understand and create meaning from banking documents. It provides the ability to carry out tasks like capturing and routing an application, extracting data, verifying authenticity, liveness assessment and facial matching, or any other task related to understanding and processing content.
Content intelligence enables digital workers to automate manual, inaccurate, people-borne processes around documents and data using machine learning and AI – something that automation platforms such as RPA cannot do efficiently and accurately alone.
Having this real-time access to information is the cornerstone to having comprehensive digital intelligence into how your business is operating while fueling more positive outcomes – like providing faster onboarding or loan approvals.
Integrating AI, ML and NLP
The transformation of content isn’t just about digitising documents. Banks must be able to automatically extract not only content, but intent and meaning from documents. Using a combination of technologies like AI, machine learning (ML), and natural language processing (NLP) and integrating them into business-critical workflows can help banks quickly consume and process both structured and unstructured documents.
This can minimise manual steps and reduce the need for making redundant requests of the customer, freeing up more time for employees to focus on higher-value tasks like service, protection, and mitigation.
While bank branches probably won’t disappear during my lifetime, I believe they must face a never-ending evolution if they want to succeed in the new technological age.
Banks must adopt a strategy with the right technology platforms for automation that empowers them to respond to the rapidly changing market, makes better use of their content, and helps them meet and exceed their customers’ digital demands.
About the author:
Cheryl Chiodi leads the financial services team at digital intelligence company ABBYY.
Cheryl is an experienced author and speaker on financial services industry trends and frequently delivers keynotes at the Wall Street Technology Association and The Taiwan Academy of Banking and Finance.
