AI helps banks to leverage their big data to recommend relevant products, services, and deals to their customers.
Banks are teaming up with fintech and software companies to provide technological capabilities they do not possess.
Banks are using machine learning models to monitor a customers’ credit history.
ARTIFICIAL INTELLIGENCE FOR THE BANKING ECOSYSTEM
If there is one industry in which advanced technology has made a significant impact, it’s the finance sector. For years, the finance sector has been dominated by traditional, ‘legacy’ banks. Who’ve built a reputation for long, slow and tedious processes. But with the introduction of digital banking and fintech, there has been a shift and many banks have embarked upon their digital transformation journeys.
One of the main reasons for this shift is artificial intelligence. Innovation in AI and similar technologies has not only disrupted the way we bank. It’s has also changed the way banks interact with their customers. A large number of customers are digital savvy. Studies show that a majority of people, especially millennials (who form a huge chunk of the current global population) prefer to interact with their banks on their mobile phone. This has forced financial institutions to go digital. Conversations are online. And so are transactions. Even, financial management services are available through digital interfaces.
The scope for AI technology in the banks is large. We examine four potential areas in this article.
Personalized channels of conversations
At the moment, personalization seems like only a buzzword. Major retail, travel and hospitality players are trying to ‘personalize’ customer experiences with the ultimate goal of increasing revenue. But for banks, it works a little differently. For them, personalization is a tool for engaging their customers in direct and open conversations. Thereby building trust in their brands and growing customer loyalty. Of course, the revenue automatically follows.
The Boston Consulting Group have estimated that a bank can garner as much as $300 million in revenue growth for every $100 billion it has in assets. All by personalizing its customer interactions.
This is where a customer’s historical data comes in handy. It’s no secret that banks have a huge amount of data at their disposal. Between customer demographic details, offline and online transaction data, credit card spends, website analytics and merchant data, they have access to quite a bit of information. They have enough data to not only predict consumer trends but personalize banking experiences for each of their customers. Through both digital and offline channels.
With a little help from AI, banks can leverage this big data to recommend relevant products, services and deals to their customers. In short, offer personalized recommendations based on their own financial behavior and personal tastes.
And in order to carry out these massive personalization projects, banks are looking towards collaboration. They’re teaming up with fintech and software companies to provide technological capabilities they do not possess. Companies like Optimizely, Braze and Crayon Data offer the financial sector the means to personalize a least a part of the customer experience. Crayon’s proprietary AI-led recommendation engine, maya.ai allows banks to create personal digital experiences for their customers. With the help of machine learning algorithms.
Customer service via chatbots and virtual assistants
Banks, especially traditional ones, have enormous customer bases. But let’s face it. They also do not have the necessary manpower nor the time to trouble-shoot day to day problems for each and every one of their customers. This could all change with the help of conversational AI.
Conversational AI, for a quite a while, has been viewed as a cost-effective way to interact with customers. According to a study conducted by Juniper Research, chat-bots can save at least 4 minutes of a customer service agent’s time. While saving 0.70 USD per query, in the process. So it’s no wonder that conversational AI has already become the favourite solution for effective customer communication among banks. Chatbots can easily handle problem-solving tasks such as responding to FAQs or handling simple account services and payment requests.
However, there are several banks who are thinking beyond the humble chatbot. By introducing virtual assistants that interact with customers through a voice interface. For example, the Swiss bank UBS partnered with tech giant Amazon to integrate its “Ask UBS” service with Amazon Echo.
Like UBS, other banks are also building virtual assistants. By integrating the right historical data, and applying the principles of predictive banking they can offer a range of services. Including expenditure tracking and analysis, personalized financial advice and predictive spends.
Securing digital banking practices
We already know that banks have access to large amounts of data. Including their customers’ personal information and credit/debit card details. Moreover, a majority of internet users transact online. Which is why banks are expected to provide secure and reliable means to carry out these transactions. So, they are turning to AI in order to give their customers safer online banking experiences.
AI systems can be used to protect customer information against cyber security threats including malware, hacking, phishing and ransomware. By using cognitive fraud analytics, a machine learning model can be trained in real-time behavioral profiling. And subsequently flag any suspicious behaviour. Since these models look at customer behavior patterns instead of specific rules, AI-based systems are more likely to detect fraud than manual monitoring.
One such bank already implementing this is CITIBank. They’ve partnered with Feedzai, a global data science enterprise to identify and eradicate fraud in real-time.
Automated credit-scoring and loan processes
Artificial intelligence is not a solution for merely automating menial and repetitive tasks. AI systems can be trained to take business decisions. Decisions which would normally require a certain level of cognitive thinking.
Banks and credit scorers are using machine learning models to monitor a customers’ credit history. And make informed decisions on loan approvals. The models can score potential borrowers on their ‘creditworthiness’ by factoring in alternative data. This data could include social media/internet activity. Like the websites visited and online purchases. By analysing the online behavior of a borrower, these models can predict the most credit-worthy candidates for loans. And also predict who is most likely to default.
However, many debate about the ethical side of an AI-based credit scoring model. Would such a model be humane enough to make the right decisions when the need arises?
They might be so. Because the AI model provides more nuanced data evaluation. They also take into account data that would seem irrelevant in the traditional credit-scoring system by FICO. More importantly, an AI model is self-learning. It continuously improves itself when new data is fed into the system. Thereby rendering the model more accurate with every use.
Paving the way for the future
Banks have become so much more than just a building that keeps customer’s money safe. They’re are looking to become an indispensable part of their customers’ lives. Right from transactions to credit cards, loans to investments and financial advisors.
AI and machine learning technology allows banks to turn this vision into reality.
By partnering with tech companies and fintech start-ups, the industry is creating an open ecosystem for both corporate and personal finance. One which is based on innovation and collaboration. With every new and successful idea, banks are becoming more robust, greener and friendlier. As a result, these practices reduce potential risks. As well as aid human intelligence by increasing opportunities for all.