Article | April 6, 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.
Article | February 25, 2020
Fintech is one of the fastest-growing industries, owing to the rising penetration of internet users. There is a paradigm shift to mobile devices for performing financial transactions and related actions. Behind the booming fintech market, there are several technologies that are contributing to making the system fast, secure, and scalable. One such technology is Artificial Intelligence (AI). The AI in the fintech market is estimated to reach USD 35.40 billion by 2025 ( Mordor Intelligence).
Article | April 7, 2020
The CARES Act recently passed by Congress funded financial assistance for small businesses experiencing economic hardships caused by the COVID-19 pandemic. Two Small Business Administration (SBA) loan programs established or expanded by the act are of particular interest to family physicians: the Economic Injury Disaster Loan (EIDL) and the Paycheck Protection Program (PPP).
Article | April 14, 2021
The rise of buy-now-pay-later in the consumer world provides a powerful example of what embedded finance at the point of need can do for brands.
Consumer debt ethics aside, B2C buy-now-pay-later providers can proudly point to the fact that implementing a BNPL solution can increase basket sizes for retailers by 20-30%. The concept, although around for years in various forms, has now been delightfully served as an option during the customer’s checkout process and the marketing has been simplified and on point.
Even as merchants must pay for the privilege of offering BNPL (keeping it interest free for customers) upwards of 4-6% per transaction, the benefits have been too good to resist.
Although let’s face it, regulation aside (there are significant hoops to become licenced in B2C finance), underwriting to consumers is easier than extending credit to businesses thanks in part for the need to understand factors such as business servicing capacity, shareholder control, bespoke pricing, industry risk and not to mention the exposure risks associated to loaning out more significant totals.
Therefore B2B lending speed and embedded innovation naturally lags behind B2C. That said, there have been remarkable steps forward over the last two years as more and more millennials and digital natives take over business buying decisions.
Existing B2B finance models
For many B2B suppliers or merchants, a referral programme to a business finance provider is nothing new.
The benefits can be obvious, especially as the alternative is to embark in to the dangers of offering trade terms and all the cashflow and collection troubles that come with it. By introducing business finance, suppliers can get paid upfront and those pesky risks evaporate.
When used effectively in a negotiation, financing offers can also allow suppliers to remove expected buyer discounts. Total order values and loyalty increase (as in the consumer world) and accessible finance even helps a buyer’s businesses become more successful — resulting in increased volumes of purchasing.
Still, referral programmes of this nature have their problems. Even with same day B2B funding available, finance applications can hold up the buying process.
This means suppliers who are keen to leverage the buyers peak interest, antagonising don’t close sales as quickly as they could and run the risk of the of the application declining or extra customer frustration and fatigue.
While the benefits for introducing finance in to the sales process still outweigh the negatives, more improvement in the experience can be achieved.
Imagine a situation as a B2B supplier or merchant where you can provide a pre-approved finance offer straight to eligible businesses through your own CRM.
This level of proactivity provides you with a powerful tool in drawing in loaded sale opportunities from customers who are primed and ready to transact. The advantage over your competition is significant as your customers already know they can painlessly purchase from you once they’re ready for more stock.
A new revenue stream also opens for your business as your FaaS provider hands you commission for each loan a buyer takes.
This example highlights the power of pre-approving customers in a proactive capacity. For this level of FaaS to work a customer permission gate is required, either as part of a customer onboarding/sign-up process or through invitation.
With a white-labelled or co-branded experience, buyers can securely connect via open banking through a third party (FaaS provider) and be reassured that no visibility of this data given to their supplier. Other business data sources (e.g. payment or accounting software) can also be connected at this point.
Through providing these connections through the supplier or merchants portal, buyers can sit back with the knowledge that they will be notified if and when they qualify to purchase goods or services on finance. Once pre-approved, sellers can push a message to their buyer from their CRM system along with any sales pitch or offer they like.
Convincing buyers to give this connection permission in the first place is a challenge that clever marketers and UX specialists will need to overcome.
Embedded at the point of need
Again, we only need to take BNPL as an example of an embedded finance solution served beautifully at the point of need. Buyers, motivated at the time to transact, are more likely to have the determination to apply for finance within the same CX process.
Unlike traditional loan referral schemes that remain disjointed with long and possess awkward handover points, through FaaS, an embedded financial product is especially attractive when done right. It can merely feel as a simple ‘way to pay’ option in the buyer experience. No application forms; no double entry of business data; no waiting.
The challenge therefore is to create seamlessness, integrated and quick decisioning. Again, still a relatively tall order for B2B finance especially the higher the loan total is (despite advances in innovation and same day funding).
The potential for more instant decisioning will need to be enabled through a) improvements in API sharing and open finance analytics to confident business underwriting and b) buyer adoption on online services and platforms.
This later point can already be seen in the growing gulf of funding accessibility between online and manual powered businesses.
For example an eCommerce buyer, generating rich volumes of data through online sales and excessive B2B Software-as-a-Service (SaaS) use has the perfect data points that can be integrated in to support lending applications. In contrast, a blue collar business, with a limited online presence and who only drops a shoe box full of receipts to their accountant every 90 days, will find it more difficult to provide the data required for quick decisioning.
The new normal
Embedded B2B finance is here to stay and serious FaaS providers are emerging.
More and more suppliers and merchants will be leveraging the power of this new technology trend to create a more successful, loyal and satisfied buyer. Through proactive pre-approvals, B2B sales teams can be armed with more empowered conversations and unique advantages over the competition.
Alternative business lenders operating as an embedded finance FaaS will also benefit by leveraging the scale of the B2B supplier and merchant channel, accessing more businesses through wider mass distribution.
Soon, simply plugging in an embedded finance solution in to your existing CRM system will become a painless experience. B2B lending products of the future, through speed of underwriting and integrated data points, will be commonplace as an attractive option during the sales process.a