Article | December 10, 2020
Customers in the financial services industry want personalized experiences. They, in fact, expect and demand them from their service providers. They prefer to stay loyal to a company as long as they receive this special treatment. As a result, personalization has become the number one priority for marketers in the industry today. They are waking up to the realization that delivering personalized experiences highly depend on understanding customer data.marke
Very few companies have the means to understand this data and use it to enrich the customer experience. The technology that has been recently making waves in every industry is known as the Customer Data Platform (CDP). A CDP is a packaged SaaS (software-as-a-service) product that is designed to build a unified customer database for an organization. Implementing a CDP can help achieve consistent customer engagement, increased loyalty, and higher sales.
David Raab, CDP evangelist and Founder of the CDP Institute, was invited as a chief guest at the Customer Data Summit 2018 event organized by Lemnisk.
David is a widely recognized thought leader in marketing technology and analytics. He was one of the first people to recognize that digital marketing systems were not just proliferating but also the data that these systems were throwing up were getting grouped into silos, making it really hard for marketers to understand customers holistically.
David also realized that there was a tremendous opportunity if he could bring these disparate systems together. Around this insight, David coined the term CDP and founded his institute in 2016. The CDP Institute’s work has been seminal in helping marketers understand the need for a CDP and the ways that they can derive value from it.
David’s thought-leadership session imparted the following key insights:
The most challenging barrier to Marketing Automation success is data integration between the various marketing systems of an organization. Financial marketers in Asia face the same challenges as their peers elsewhere, which include unifying customer data, providing superior customer experience, working within compliance constraints, and finding the budget to pay for solutions.
The CDP industry has seen a good growth rate of around 73% over the last 12 months. Two-thirds of the growth is attributed to new vendors and the remaining to existing vendors. The adoption rate has been high for B2C marketers as their businesses depend highly on user engagement and digital conversions. Companies that opt for a CDP prefer to have a complete packaged solution that includes the core CDP functionality along with analytics and engagement.
A CDP works well when all marketing systems are interconnected. One interesting observation is that one-third of CDP users lack an integrated technology stack. Companies that claim to have a CDP do not have this system integration and, therefore, do not fall under the CDP-classified vendors.
Things such as churn prediction and predictive modeling are a set of classic algorithms that thrive on good data. Artificial Intelligence (AI) is totally data-driven and works well with data that is highly detailed. A CDP can play a major role in developing custom algorithms and advanced intelligent systems such as AI. One of the things that it can do is create a standardized variable or model score and make that shareable to all systems that it connects to.
Of its various capabilities, a CDP also enables cross-device personalization by associating each device with the customer’s master ID when they log in. The master ID is used to build a unified customer profile with all device data. The right message for each master ID is selected and shared with all devices. The unified and complete customer profiles help financial marketers in selecting the right message and deliver a consistent experience across all devices.
It is still early days for a CDP in Asia. Many organizations are still at the stage of learning for themselves why options such as DMP (Data Management Platforms), Enterprise Data Warehouses, and marketing clouds won’t solve the problem that a CDP addresses. The core technologies used in Asian financial institutions can support any level of marketing sophistication that their users are ready to deploy. The early CDP adopters are touted to have an advantage over others in the industry.
Article | December 10, 2020
It’s a fact: to succeed in business, sometimes you need to borrow money to keep cash flow steady. But what do you do if you have bad credit? What business financing options do you have? Not to worry. Even if your business and personal credit scores are too low to qualify you for traditional business financing, you still may qualify for a secured business loan with the right lender, also called a collateral business loan.
Article | December 10, 2020
The global payments landscape is undergoing a massive reorganization. Industry researchers and analyst groups attribute this seismic change to many factors. Technological advancements and competitive forces have proved to be the biggest transformational forces in the payments industry that have combined together to meet both consumer demands and standard banking regulations.
Article | December 10, 2020
The world is digitizing, and the world is digitizing because we’re seeking low friction and immediacy. We want immediate responses; we want stronger commerce connections that can scale up to more rapidly. So, within that framework, one can’t expect banking and financial services to stay the same as it has been, because ultimately it has to shift.
Artificial Intelligence is bubbling with a lot of energy at the moment, and so is Fintech. There has been a lot of investment going on in it, and it’s under so much spotlights. The rate of innovations and the abundance of new technologies have sprung up everywhere. Things from artificial intelligence, peer to peer lending, big data, block chain, crowd funding, digital payments, and Robo advisors, just to name a few.
We need to think about FinTech with two capitals T’s that is, TECHNOLGY and TRANSPARENCY. It’s more about technology, enabling the banking industry to do the wonder, and Transparency because it’s a sector where customers can make much more informed choices. But what has made Fintech go so unmask is just the pace of innovations in this space. FinTech has now moved from prevention to resilience. We are just at the tip of the iceberg.
Globally, the value of an investment in Fintech companies amounted to approximately 112 billion U.S. dollars in 2018, which was a record high for the sector. The annual value of global venture capital investment in Fintech companies is increasing and doubled between 2017 and 2018.
This is an industry that is hungry for change because the consumers are hungry for change, and so the big corporations, the incumbents are also ready to change. Consumers want seamless, frictionless experiences. They want all the pain points removed from their banking journey.
Table of Contents
• Artificial Intelligence- Paving the Way for the Future in Banking
- Embracing Conversational AI in Banking
- Driving Personalization in Banking through Artificial Intelligence
- AI-Model for Automated Credit-Scoring and Loan Processes
- Transforming Wealth Management with AI
- Utilizing Robotic Process Automation Software in Banking
• In Conclusion
Artificial Intelligence- Paving the Way for the Future in Banking
Artificial Intelligence has the potential to revolutionize how consumers and businesses handle financial transactions. There will surely be hits and knocks along the way, but AI is not going away anytime soon. It is the future.
FinTech companies want to deliver personalized and cost-effective finance products. To do so, they need to utilize large numbers of data from various touch-points. Introducing the financial sector with advanced techs like big data, artificial intelligence, and blockchaincan facilitate banking and finance go far beyond cashless payments and mobile services toward personalized customer experience that will transform FinTech in 2020.
Financial institutions now know their customers' behavior and social browsing history. The accelerated rise of Artificial Intelligence and machine learning has resulted in banks being able to reduce the number of operations as they embrace the power of automation. Artificial Intelligence facilitates real-time omnichannel integration of these insights to deliver a personalized one-to-one marketing experience for their customers.
AI’s potential can be looked at through versatile lenses in this sector, especially its implications and applicability across the operating landscape of banking.
Learn more: https://www2.deloitte.com/content/dam/Deloitte/uk/Documents/financial-services/deloitte-uk-world-economic-forum-artificial-intelligence-summary-report.pdf
The three main channels where banks can use artificial intelligence to save on costs are front office (conversational banking), middle office (anti-fraud) and back office (underwriting). Let’s explore more on how banks can use Artificial Intelligence to constantly innovate at scale:
Embracing Conversational AI in Banking
An artificial intelligence feature that is redefining customer engagement is conversational AI. It has been viewed as a cost-effective way to interact with customers. Nowadays, conversational interfaces represent one of the biggest shifts in banking user interfaces to date and are modifying how they obtain and retain customers and enhance their brand identity.
According to a study conducted by Juniper Research, chatbots can save at least 4 minutes of a customer service agent’s time. While saving 0.70 USD per query, in the process. Conversational AI has now become the preferred solution for productive customer communication among banks.
The universality of messaging apps, like Facebook Messenger, WhatsApp, Slack, Microsoft Teams or SMS, and the adoption of voice-activated assistants such as Amazon Alexa, Google Home, or Apple’s Siri are bringing conversations back into our banking experiences.
Conversational Banking Experience
For example, the Swiss bank UBS partnered with tech giant Amazon to merge its “Ask UBS” service with Amazon Echo. Customers can communicate with multiple banking processes, via the chat interface, such as reporting potential fraud on their banking cards, applying for an increase on their credit card limit, or getting a breakdown of their recent transactions, and more.
Driving Personalization in Banking through Artificial Intelligence
Customers need banking on the go. They are looking for more personalized experience and expect to transact with banks from the convenience of wherever they are. Data advises that businesses that offer personalized services achieve far better business outcomes. Giving the right individual experience through the right channel at the right time can make banking more personalized. AI can play a significant role in assisting banks to understand customer behavior by leveraging transactional and other data sources.
The Boston Consulting Group has 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.
• Artificial Intelligence enables banks to customize financial products and services by adding personalized features and intuitive interactions to deliver meaningful customer engagement and build strong relationships with their customers.
• Artificial Intelligence enables a higher degree of personalization and customization by tapping into information such as customer behavior, social interaction, and even health or important event dates, all to create a well-rounded picture of their customers’ profile.
• AI can classify prospects based on financial capability, family size, etc. and offer tailored products.
To carry out extensive personalization projects, banks are looking to collaborate. They’re now teaming up with fintech and software corporations to provide technological capabilities they do not maintain.
In 2019, the total value of transactions in the personal finance segment will amount to $1,092,496 million according to Statista. Remarkably, the market’s largest segment is robo-advisors, with total assets under management of $980,541 million. In 2023, the number of people using robo-advisors is predicted to be 147 million.
Organizations like Optimizely, Braze, and Crayon Data offer the financial sector the means to personalize the customer experience. Crayon’s proprietary AI-led recommendation engine, maya.ai, allows banks to create personalized digital experiences for their customers. All that with the help of machine learning algorithms.
AI-Model for Automated Credit-Scoring and Loan Processes
Artificial intelligence not only automates menial and repetitive tasks. It can be trained to take business decisions that normally require a specific level of cognitive thinking. Lending and credit scoring are the critical business for banks and directly or indirectly touches almost all parts of the economy.
Banks always relied on models and experts to make effective credit decisions. Now models are becoming sophisticated enough to replace experts. Banks and credit scorers are employing machine learning models to track customers’ credit records. And make well-informed decisions on loan approvals.
Banks and credit scorers are employing machine learning models to track customers’ credit records and data. And make well-informed decisions on loan approvals. The AI-based credit scoring model can score potential borrowers on their ‘creditworthiness’ by factoring in alternative data. The more data available about the borrower, the better you can assess their creditworthiness.
This data could include candidates' social media/internet activity and websites visited and online purchases history. By examining 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 back out.
In the new digital reality, AI-powered credit decision permits lenders to:
• Fast and secure loan origination process
• Automate borrower`s digital journey
• Find and filter unfit borrowers based on sophisticated proprietary models powered by deep neural networks
• Lessen the operational costs of origination
• Authorize unhindered scalability of the lending business
Transforming Wealth Management with AI
Wealth managers are positively deploying artificial intelligence (AI) to answer the needs of a new generation of tech-savvy high net worth individuals.
According to the 2018 Asia-Pacific Wealth Report (APWR) released by Capgemini, the APAC region witnessed a 12.1 percent growth in HNWI population in 2017, and a 14.8 percent rise in wealth, with the region, now forecast to exceed US$42 trillion by 2025.
One of the AI trends in wealth management is the potential for the technology to move beyond traditional tasks, such as KYC and risk management, to new centers of enhancing relationship management and client experience.
On the one hand, firms are investigating how they can make their relationship managers more productive. On the other, the new generation of clients wants predominant online services, assisting banks to examine how they can optimize their digital offerings.
“Consumers’ and SME’s behavior and needs are changing fast,” said Rosali Steenkamer. There is an immense data explosion with structured and unstructured data. Only big data-driven models, Machine Learning algorithms and Artificial Intelligence can tackle this to serve the right solution to the right customer. Traditional technology is simply not able to deal with these challenges.
-CCO and Co-Founder at AdviceRobo.
Relationship Managers are not motivated to capture datasets. The only solution is to encourage the front office to collect new data, as well as collaborate with colleagues who develop AI-powered products and services. Doing this will drive productivity for Relationship Managers and an enriched experience for their end clients.
Everyday tasks can be handled by AI systems, releasing wealth managers to concentrate on higher-level investment strategies. AI systems can also analyze client data to adequately create packages prepared for specific financial and social demographics. Utilizing AI in finance expands service offerings while also making them more customizable. With a variety of AI tools at their disposal, wealth managers are outfitted with the research and data insights essential to make quicker, more informed decisions for various clients.
Learn more: https://capital.report/blogs/how-fintech-is-shaping-the-future-of-wealth-management/8244
Utilizing Robotic Process Automation Software in Banking
This year robotic process automation (RPA) will continue to impact financial institutions, to help them be more efficient and effective, as well as help ensure they meet federal and state compliance requirements.
RPA is growing rapidly. Recent RPA trends and forecasts anticipate that the market for robots in knowledge-work processes will reach $29 billion by 2021. For the banking industry, robotics outlines a unique and underutilized way to increase productivity while minimizing traditional repetitive and manual-labor-intensive processes.
The accelerated rise of AI and machine learning has resulted in banks being able to reduce the number of operations as they embrace the power of automation. AI facilitates real-time omnichannel integration of these insights to deliver a personalized one-to-one marketing experience for their customers.
So, when we look at these phases of development in the Banking Industry, we understand that it’s not just about inserting technology into banking; there is a larger shift here. Part of the shift is around trust and the utility of the bank. Artificial intelligence and machine learning technologies allows banks to turn vision into reality. Whether you are ready for it or not the AI revolution is poised to provide exciting avenues for innovations.