Money creates a chain reaction; the more you have, the more you can earn by managing it. But it can have an adverse effect if you don't have it. This is also valid for data. Large amounts of information enhance banks' ability to support customers, but financial institutions need to know how to use it.
Today's banking customer is in dire need of guidance from banks, whether on spending, savings, loans, planning, or all of the above. After all, two out of three Americans Today they struggle with managing their finances.
Also, their allegiance changes easily, considering that neobanks are more accessible with instant onboarding processes. Modern banks face the challenge of becoming familiar with their customers, delving into the reasoning behind their financial decisions and improving their loyalty.
Yet without knowing what data to look for and how to understand the individual needs of their clients, blanket approaches and loosely categorized consumer profiles leave clients excluded from adequate financial support and in the same financial position, if not worse off.
The question that arises is how modern banks can then use the data and build trust to improve the financial health of the consumer.
Key pain points for modern banks
Banks should recognize that the past financial history and characteristics of those categorized as similar to each other merely represent preliminary reflections of the client in question.
Let's say a young woman became interested in a $1,000 coat. Her algorithms told her that women her age bought this and her system started sending notifications for BNPL extension. However, what happens if the woman loses her job? What if she can't make her payment? BNPL extension?
BNPL extension it can be a convenient way to make large purchases at attractive interest rates, but in an emergency, you might resort to making credit card payments. This would extend the life of that debt BNPL extension and also add additional interest. Even if you find a new job, you may have faced further financial hardship, which negates the benefit of BNPL extension.
This is the whole picture. Open banking provides fintech banks with information from their customers' main accounts to tell them where they shop, how much they spend on certain products, whether they have a car and information about their family. However, staying on top of the latest data protection regulations means you have to constantly readjust operations.
For a consumer to share their life with the bank, they must first understand the real benefit of doing so.
Modern banks must ensure that they comply with privacy and security regulations so that their customer data is safe. According to the legislation, banks must strictly use the data for reasons agreed with the consent of the person. They must ensure that consumers understand how their bank uses their personal information with third parties.
Here are three steps modern banks can take to address their data pain points.
Data to determine financial fitness
It is key that banks understand the correct metrics to identify the capabilities of each individual. By looking at transactional data, spending habits, and customer behaviors, banks can recognize recurring patterns and better align their products.
No matter the financial status of a client, if he can pay all his loans and bills on time, he is an ideal candidate for a financial institution. Banks can offer adequate loans that benefit the bank and the client instead of initiating high interest rates and, therefore, victims of eternal debts.
In cases of low credit risk criteria or no transactional data, disruptors find ways to understand the financial strengths of their customers. For example, the United States Consumer Financial Protection Bureau (Consumer Financial Protection Bureau, CFPB) designed a survey to assess individual financial well-being. The process uses a set number of interviews to ensure that people know what they are being asked, and provides feedback to users in the form of a value proposition each time they take the test to compare user performance and the offering.
For example, start learning a new language: financial aptitude tests take a similar framework. Like the CFPB, you provide content to the client and the client answers questions to the best of their ability.
Machine learning then assesses users' aptitude levels based on their responses, and will increase the difficulty levels for subsequent questions accordingly. The time it takes to fully understand the client depends on the variability of responses. After a few days or weeks, the responses start to level off and banks can provide conveniently affordable and adjusted loans to their customers.
However, banks cannot assess fitness without training the person. Depending on your base customers and behavioral data, you may find that your customers have a particular set of insights, and you should tailor your assessment to them.
For example, a bank for trucking customers. Many truckers take out advance payment loans. So in the training that the bank offers, I would explain the advance payment and then ask if they need it in the aptitude test. You can also ask them if they think the help from this loan will reduce their absenteeism.
The answers would show a deeper understanding of the benefits of the loan and increase your eligibility. As a bank, it is essential to test the consumer's relationship to the problem to fully understand their needs and capabilities.
Banks can wean themselves off reliance on outdated credit scores by using transactional data, financial wellness surveys, or AI-powered aptitude tests.
Client-focused advice
Financial institutions focus on customer-centric, rather than product-centric, support. By allowing data to generate the persona instead of categorizing customers based on predetermined groups and judgment calls, banks can identify behaviors and capabilities to offer personalized products.
There are hundreds of data collection points that allow the AI to create a customer profile and incorporate new units of information in real time. However, there will be transaction patterns that do not make sense and the need for human intervention will remain in these situations.
Imagine a customer who flew to the same city three times in one month. They may have been visiting a sick relative, going to a work conference, or simply setting up a new office, and it is this reason that will determine how often they travel in the future and why additional information is needed. Without context, personalization cannot work effectively.
But it's not just about the data that's already available. Sales support advisors must actively ask their customers why they make these trips. Various groups of people are better at recognizing various customer pain points. For example, if the client who flies to that city visits a sick relative and only speaks Spanish, a sensitive Spanish-speaker will obtain much better results than what a generically expressed sales pitch in another language may seem.
There must be cross-platform integration and diversity within banks' risk organizations to improve the financial health of today's eclectic marketplace. A diverse workforce can also better train chatbots to capture contextual information, and humans can read the logic of the obtained pattern. As a fintech bank, you can rely on sales support advisors or chatbots for personal answers, but in any case, you need customer trust.
Build trust and protect customer data
For a customer to share their life with the company, they must first understand the real benefit of doing so. Building trust takes time.
Let's go back to the travel example. Customer asks: What is the best credit card to use? An advisor may respond: This is the most suitable product for a,b,c. But, we have noticed that you have been traveling a lot. What is your favorite airline? We have a card that offers x,y,z airline miles.
Trust and relationships do not develop overnight. By producing small contextual nudges over time, you can understand your audience and personalize your products. Focusing on the existing customer builds customer loyalty, and that's how you get your best leads: they start to trust the bank and pass it on to their friends and family too.
Trust is built on strong pillars, so make them particularly strong when it comes to data. APIs connect organizations and govern how information is shared, displayed, and protected. Having a control interface helps keep data safe with third parties by monitoring its use against mandatory security levels and additional requirements that you agree with customers.
Another way today's banks can protect their customers is through behavioral analytics. Tracking typing speed and tone of voice will detect suspicious behavior, while companies like Innovatrics provide biometric technology with facial and fingerprint recognition accurate. In this way, the customer can be identified from multiple perspectives, making their persona much more complex to replicate.
Provide reassurance to the customer as to why you are tracking their typing speed or asking personal questions, and how you will communicate with them; this way, they can know what to expect from the services. Onboarding processes where sales advisors or chatbots ask the customer to answer questions or approve their apprehension in stages throughout the terms and conditions will ensure clarity. And I know when you meet customer expectations, they increase trust in the brand.
Financial entities have at their disposal the most valuable data. They have more extensive details on consumer spending, behaviour, needs and aspirations than any other institution. Open banking provides banks with more efficient solutions for their customers, but this is only possible with strict APIs or security standards governing fair use and transparency. Institutions that lead in diversity within their team are making more refined assumptions about their data and are profiting at a higher rate.
Data from modern banks creates accurate consumer profiles and products tailored to the individual customer's information and trust require. But it is necessary to remember that building trust in a sector that is not trusted requires transparency and putting the uniqueness of the client and the employee first.
