Article | March 1, 2020
People know their banks and their favorite fintech applications. However, consumers are generally not aware of data aggregators like Plaid and Finicity, which collect consumer data from banks, crunch it and feed it to fintech applications. Blockchain technology can play a key role in helping data aggregators manage consumer financial data while complying with regulations and empowering consumers.
Article | March 1, 2020
With plenty of post-recession anti-banking sentiment still lingering, it’s common to see fintech and traditional banks framed in oppositional terms. There’s some truth to that, especially with disruption-minded digital-only banks, but technological innovations have transformed banking of all stripes — and nowhere is that clearer than with artificial intelligence.
Article | March 1, 2020
Over the past decades, banks have been improving their ways of interacting with customers. They have tailored modern technology to the specific character of their work. For example, in the 1960s, the first ATMs appeared, and ten years later, there were already cards for payment. At the beginning of our century, users learned about round-the-clock online banking, and in 2010, they heard about mobile banking. But the development of the financial system didn’t stop there, as the digital age is opening up new opportunities — the use of Artificial Intelligence. By 2023, banks are projected to save $447 billion by applying AI apps. We will tell you how financial institutions are making use of this technology in their operations today.
Chatbots are AI-enabled conversational interfaces. This is one of the most popular cases of applying AI in banking. Bots communicate with thousands of customers on behalf of the bank without requiring large expenses. Researchers have estimated that financial institutions save four minutes for each communication that the chatbot handles.
Since customers use mobile apps to carry out monetary transactions, banks embed chatbot services in them. This makes it possible to attract users’ attention and create a brand that is recognizable in the market.
For example, Bank of America launched a chatbot that sends users notifications, informs them about their balances, makes recommendations for saving money, provides updates to credit reports, and so on. This is the way the bank helps its clients to make informed decisions.
Another example is the launch of the Ceba chatbot, which brought great success to the Australian Commonwealth Bank. With its help, about half a million customers were able to solve more than two hundred banking issues: activate their cards, check account balances, withdraw cash, etc.
AI functionality in mobile apps is becoming more proactive, personalized, and advanced. For example, Royal Bank of Canada has included Siri in its iOS app. Now, to send money to another card, it’s enough to say something like: "Hey, Siri, send $30 to Lisa!" - and confirm the transaction using Touch ID.
Thanks to AI, banks generate 66% more revenue from mobile banking users than when customers visit branches. Banking organizations are paying close attention to this technology to improve their quality of services and remain competitive in the market.
Data collection and analysis
Banking institutions record millions of business transactions every day. The volume of information generated by banks is enormous, so its collection and registration turn into an overwhelming task for employees. Structuring and recording this data is impossible until there is a plan for its use. Therefore, determining the relationship between the collected data is challenging, especially when a bank has thousands of clients.
There used to be the following approach: a client came to a meeting with a bank employee who knew their name and financial history and understood what options were better to offer. But that's history now. With the wealth of data coming from countless transactions, banks are trying to implement innovative business ideas and risk management solutions.
AI-based apps collect and analyze data. This improves the user experience. The information can be used for granting loans or detecting fraud. Companies that estimated their profit from Big Data analysis have reported an average increase in revenue by 8% and a reduction in costs by 10%.
Extension of credit is quite a challenging task for bankers. If a bank gives money to insolvent customers, it can get into difficulties. If a borrower loses a stable income, this leads to default. According to statistics, in 2020, credit card delinquencies in the U.S. rose by 1.4% within six months.
AI-powered systems can appraise customer credit histories more accurately to avoid this level of default. Mobile banking apps track financial transactions and analyze user data. This helps banks anticipate the risks associated with issuing loans, such as customer insolvency or the threat of fraud.
According to the Federal Trade Commission report for 2020, credit card fraud is the most common type of personal data theft.
AI-based systems are effective against malefactors. The programs analyze customer behavior, location, and financial habits and trigger a security mechanism if they detect any unusual activity. ABI Research estimates that spending on AI and cybersecurity analytics will amount to $96 billion by the end of 2021.
Amazon has already acquired harvest.AI - an AI cyber security startup - and launched Macie - a service that applies Machine Learning to detect, sort, and structure data in S3 cloud storage.
Article | March 1, 2020
There is a huge transformation underway in the financial services industry. Over the past year – as a result of the COVID-19 pandemic – clients have been forced to take on more of an active role in monitoring and planning for financial uncertainty. But the big change is that these clients have become much more emotionally invested in their organisations’ financial wellbeing. In a time where everything is digital first, it’s no surprise that many clients want to be able to search for answers themselves, escalate issues quickly and receive the support they need to better navigate the uncertain economic landscape, at speed.
Of course, as clients demand a smoother and more fulfilling experience, we’re seeing a shift in how financial services companies manage their business model for success in the long term. Whilst, stereotypically, the financial services industry has been considered ‘old school’, and in many cases still lags behind other industries in the digital transformation race, the pandemic is proof in point that relying on legacy systems is just not an option for the sector anymore.
Thriving during turbulent times
The good news is that many organisations in the sector are already rising to the challenge, adjusting their products and services to meet the needs of customers who might have been struggling through the pandemic themselves.
Siemens Financial, a division of Europe’s largest manufacturing company, for example, moved quickly to scale their service to meet surges in customer needs. The financial arm provides B2B financing solutions to a large client base covering both small businesses and large corporations. When the pandemic struck, while the company was quickly inundated with requests for support, they had the right mindset and tools already in place to keep things running smoothly.
At the onset of the pandemic, the organisation witnessed a 30 percent increase in customer support ticket volumes. Like with all other businesses operating in the service industry, the team were challenged with managing a huge influx in client requests, whilst maintaining their core offering of delivering a personal service to every client. Based on a data-driven decision, the team moved its entire operation online, within 48 hours. In doing so, they were able to respond to new tickets during the peak of the pandemic within just six to seven hours, plus decrease resolution time from 24 hours to little more than eight. What’s more, they quickly moved the entire team to a remote working set up.
Frictionless digital services are paramount to remaining resilient in the face of COVID-19. Of course, for all organisations, this means saying goodbye to those spreadsheets used to track customer data and instead, embracing custom built support solutions providing real-time insights to support businesses in making decisions, at speed.
Investing now, for a successful future
However, for organisations who have more traditionally operated off of old or outdated legacy systems, it can be hard for them to visualise what a more digital way of operating could look like in practice. As you think about the road to recovery, it might therefore be worth considering where to invest first for the best return. For example, according to the Zendesk Customer Experience Trends Report 2021, 67 per cent of customers are willing to spend more at a company providing them with a good experience. Whilst it may feel like the thriving organisations are the ones investing lots of money into CX technology, it’s clear that investment - or lack thereof - is being felt by customers too.
We’ve reached the digital tipping point - where holding at the status quo will actually put companies further and further behind. It’s about equipping your employees with the right technology, at the right time. We saw that Siemens Financial could keep track of customer conversations remotely, with minimal disruption. This is because the flexible platform they used to keep track of the customer experience provided their service agents with a 360 view of all clients’ prior interactions with the team. For example, whether they’ve used WhatsApp, the phone, or email to communicate with the brand, for customer experience agents using an omnichannel platform, the conversation looks the same.