How Banks can use Artificial Intelligence to Constantly Innovate at Scale

RASHMI SINGH | April 6, 2020

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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 blockchain can 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.

 In Conclusion


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.

Spotlight

BDO Unibank

BDO is a full-service universal bank in the Philippines. It provides a complete array of industry-leading products and services including Lending (corporate and consumer), Deposit-taking, Foreign Exchange, Brokering, Trust and Investments, Credit Cards, Corporate Cash Management, and Remittances in the Philippines. Through its local subsidiaries, the Bank offers Leasing and Financing, Investment Banking, Private Banking, Rural Banking, Life Insurance, Insurance Brokerage, and Stock Brokerage services.

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FINTECH

Banking Digital Transformation in an AI-First World

Article | December 8, 2021

Every banking customer's journey is an experience in itself, and users expect the best service from the start. Technology plays a critical role in the customer's journey, and the pandemic has increased in digital adoption across the globe. Banks and insurance companies have quickly responded with digital transformation to provide customers with a seamless and personalized experience. While the customer engagement part of the digital transformation is the most talked about element in the digital transformation process, there are underlying critical aspects to a successful digital transformation strategy. Artificial intelligence increasingly plays a focal part in creating value for organizations, customers, and employees. With tech-first companies like Amazon, Apple, Facebook, and Google providing financial services to consumers, there's a significant threat to traditional institutions that do not scale to the next level of digital transformation. Banks and insurance companies in the first level of digital transformation adopt an omnichannel approach where their customers can perform the same financial transaction, whether they are on the website, the mobile app, the call center, or the bank's branch. In the next stage of digital transformation, companies need to leverage AI to efficiently automate processes by layering technology, with the focus still being on the customer. Banks and financial institutions need to change their technology structure and make it AI-first fundamentally to go beyond digital transformation. Here are critical areas where banks and insurance companies can build AI-first infrastructure to enhance their customers' experience. Document Enhancement Millions of physical documents change hands every day, and their digitization is a critical factor in the financial sector. Unfortunately, degradation issues hamper the digitization that impacts the OCR accuracy. Organizations need to deploy manual teams to extract the information, leading to delays and additional expenditure. With Visual AI, it is now possible to enhance the quality of scanned documents and make them legible again. ORBO provides an AI-powered document enhancement solution through an API or an SDK that works as a pre-processing step to OCR, boosting text extraction accuracy. AI-powered Online Onboarding Online KYC and onboarding are widely used today as part of digital transformation; however, ground-level issues can impact the customer experience. For example, a customer connecting from a village with weak internet connectivity might not complete the online video KYC due to poor video quality. Super Resolution AI for video enhancement can be deployed in the app to enhance the video in real-time, even in weak bandwidth scenarios. ICR - Intelligent Character Recognition as part of RPA OCR (Optical Character Recognition) is a software that scans text and digitizes physical documents in the cloud or on the company's premises. Google Vision, Microsoft Read API, Abby, and Tesseract are popular OCR Softwares available in the market today. The challenge with OCR is that it works mainly on structured documents. To extract data from unstructured documents like handwritten ones, banks are now deploying ICR that recognizes fonts and styles of handwriting and intelligently classifies the text information. RPA companies like Automation Edge have combined OCR, ICR, and AI-Document Enhancement to extract data with higher accuracy across various document types with minimal manual intervention leading to cost savings and better ROI. Facial Recognition on Edge (face captcha) Facial Recognition is a widely used technology, especially in the post-pandemic world. An advanced Facial Recognition deployed on edge can help provide better security to infrastructure and data. Face Captcha can become the new way of managing access to data even on edge devices without the internet. AI Chatbot Customers like the human touch, but they are open to automation as long as the information is personalized for them. For example, AI Chatbots like Yellow.ai help provide a personal chatbot experience to elevate customer service. Fraud Detection Customers and employees are increasingly working remotely due to the pandemic, and it is essential to put security checks on the office infrastructure. Face Captcha combined with detection of Shoulder Surfing can help thwart fraud across ATMs, workstations at remote locations. ML Approved Loan Processing Banks generally depend on a single credit score to evaluate the repayment abilities of the loan applicants. AI and ML models can help in alternate credit scoring methods such as metadata analysis through a smartphone that provides insight into user behavior and spending patterns, decreasing the time to process the loan. Conversational Intelligence With AI, it is possible to go beyond traditional sentiment analysis of speech-to-text. One can convert audio and video conversations into text in real-time or after the conversation have ended. Intent analysis of the customer can help in creating follow-up action items. One can also monitor agents' behavior on call and how they are talking to the customers. Automated DevOps DevOps is an impact-driven approach to delivering software, while AI brings in automation. With the combination of AI & DevOps, tech teams can now automate testing and release software in the organizations. AI can further monitor and identify issues and increase collaboration within the units. Automated DevOps can help the banking industry to scale tech integrations. AI & Blockchain AI and Blockchain solve different problems but can be combined to improve processes from support, onboarding to payment processing. For example, opening a stock trading account can take several days because the company needs to collect information from various sources about the customer. All customer information can be stored in a blockchain for AI to quickly analyze and make robust decisions. As a result, financial institutions can offer personalized services to more customers faster and efficiently. Building a digital transformation stack for tomorrow requires financial institutions to bring an AI-first vision that joins all aspects of the business while placing the customer at the center. It also needs agility in adopting and leveraging newer technologies enabling a high-speed data streaming channel across functions. Banks need to move away from traditional complex structures to lightweight workflows by rebuilding their core technology and data infrastructure to support AI-powered decision-making in an AI-first world focused on the customer.

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FINANCIAL MANAGEMENT

High Frequency Traders: Masters of Modern Financial Markets

Article | November 16, 2021

High Frequency Traders (HFT) play a ‘need for speed’ game day after day in a quest to capture smaller and smaller profits. To be a successful high frequency trader there are two mountains to summit. The first, is the development of a system which can analyse data in as close to real time as possible. Here, this would involve not only monitoring prices, volatility, clustering; on multiple exchanges simultaneously. There is also the need to monitor slower items such as fundamentals, news streams and annual dividend pay-outs. It is likely that many high frequency traders are using artificial intelligence to do this, as they must process big data sets in as close to real time as possible. Thus, this need for speed has started an ‘arms race’ between traders to possess the fastest system in the market. This links to the second of the two challenges: the minimisation of latency, or in plainer terms; the maximisation of speed. Information must be processed as quickly as possible, a trading strategy devised and a market order submitted. Ideally, quicker than any other trader. To be clear, there is always a first movers’ advantage when trading information into price! To give a sense of this speed, consider that all of the above can be achieved in less than the time required for a human being to blink. This technology originates in the digitisation of exchanges, for example the NASDAQ exchange opened in 1971 as the first electronic exchange. Later in the 1980’s, proprietary information services became available. The most well-known of these became Bloomberg which began in 1981. The faster trading conditions and greater availability of information has opened up the gap between human traders and algorithmic traders, which has become wider to a point where their ability to interact with each other is questionable. Estimates vary; however, one reliable source suggest that 73% of the US equity trading is now undertaken by HFT’s . In my own research in ForEx markets for highly traded pairs, the HFT incidence can be up to 8%, even in an over-the-counter market. To illustrate an issue HFT activity creates, consider that a high frequency trader can observe information, identify which asset is involved and the new ‘correct’ price, and submit an order; all in the time needed for a human trader to blink. This mismatch is especially problematic where liquidity is provided to markets by high frequency traders. Here, it is very difficult for slower human traders to access liquidity as it does not remain in the market long enough as HFT matches against HFT. In their defence, a high frequency trader would tell you that they provide liquidity for rare assets and also work to keep bid ask spreads tight. Also, it is not debateable that HFT does make a significant contribution to keeping prices correct, in terms of reflecting all known information and holding the law of one price across trading venues. So, it can not be as simple as concluding that HFT is all bad and progress should be revered. At this point we need to know that a HFT is typically an inventory neutral trader who will trade to capture spreads. Typically, individual trades earn very little return. Where the spread capture takes place over short time periods; this is known as a ‘scalping’ strategy. A trader who can work at a sufficiently low latency, will be able to run a scalping strategy over six ticks of the market which in some cases, can occur in less than one second. High Frequency Traders in their efforts to capture spreads could also increase price volatility. This may especially be the case where a trader, is placing a large order which incentivises HF traders to try and provide liquidity whilst capturing a spread. As this increases the price impact of the large order, there is an incentive to find a way to avoid the HF trader’s attentions. Such places exist away from the regulated exchanges; these off exchange liquidity sources are known in the trade as ‘dark pools’. These remain legal for trades above a certain size. A more pertinent issue in terms of the fragmentation of markets, is the creation of trading venues which seek to protect slower traders from predatory high frequency traders. Many regulatory tools have been proposed to constrain HFT activity. National regulators have over the last decade developed governance standards for algorithms used in trading, to incentivise developers to have full awareness of the behaviours of their creations under the full range of market conditions. On balance, I am not in favour of solutions involving lags or generally slowing down trading, as the race to the front of the queue still exists. Rather, any intervention needs to alter the desire/incentive for some activity to take place. For this reason, minimum order resting times, perhaps as little as one whole second would remove the incentive for aggressive scalping strategies.

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How to Tap the Power of Strategic Alliances and Partnerships?

Article | November 2, 2021

Why form strategic alliances and partnerships? Strategic alliances are key to business growth. They bring our strengths together to serve our clients better. I believe that each partner offers unique benefits to tap into and help achieve value at scale that the digital world demands. The North Star that guides our partnerships is our purpose to engineer sustainable businesses to improve everyday life. The partner ecosystem strives to bring end-to-end solutions to our clients and to continually infuse innovation as well as best-of-breed thinking to influence business outcomes. To that end, we constantly explore the potential of emerging technology, growing our relationships and building our partner network so that we bring fresh ideas and custom-made solutions to better serve our customers. How to establish winning alliances? The heart of building and nurturing alliances lies in designing and managing them to foster collaborative behavior. It also requires being aware as well as being able to mitigate the factors that can result in alliance failures. A successful partnership is pivoted on the winning formula of, “1+1 is greater than 2”. We work with our strategic alliances, the recognized leaders as well as the disruptors in the industry to generate Return on Investment (RoI). Best practices to develop and nurture a differentiated alliance: 1. Laser focus on who and how to partner: While it may look good to have a vast number of partners tagged to one’s brand, it is often an ineffective approach. It simply spreads the focus too thin. I believe in working with a fewer number of strategic partners, understand their operating model, craft a solid go to market strategy and implement it with the endorsement from executive sponsors. This means, aligning on a singular vision – agree on the impact we want to co-create and then formalize an execution plan to co-sell. It is equally important to highlight the strength of alliance to all the stakeholders across the business. While the fundamentals of how to run an alliance remain the same, each vendor is unique: they vary by industry, region, size, complexity, and hence require a bespoke approach. Strategic alliances are not a one-off transactional relationship, but a long-term view of how the partners can generate synergies to help address clients’ pain points. An added dimension to achieving partner success is the “three way” or “triple play” partnerships. An alliance of three partners with complimentary strengths and assets can yield competitive advantage. 2. Leverage the power of networks:An alliance success is often determined by the power of networks. Trust is the heart of any network. A well-run partner ecosystem relies heavily on the people driving it. Professional networks in the industry across geographies and executive connect at both business and technology level are pivotal to run a well-oiled partnership engine. I have observed that successful alliance leaders not only cultivate relationships across functional areas internally, but also within the alliance organizations across the regions and with the clients and prospects. Even if one’s purview as an alliance leader is a certain partner (industry and geography), it is important to be able to deal with the cultural differences. This is true especially when engaging with the stakeholders across the organizations and regions. It is getting even more relevant in today’s hybrid mode of work. 3. Partner Up: A consistent communication with our partners helps us uncover potential opportunities early in the sales cycle and iron out any differences. Regular partner governance ensures legal compliance and privacy imperatives are embedded from the beginning. This allows us to not only jointly solve the challenges our clients grapple with but also to co-innovate. Cadence also helps organizations to predict demand, timely invest in training, certifications and overall enablement of associates to be delivery ready. After winning business together, partners should look to leverage these successes with the support of marketing. Partner marketing can elevate and amplify the visibility of alliance across social channels. The impact can be seen in terms of accelerated lead volume, higher deal velocity and expanded deal size. Simply put, it improves the overall health of the pipeline. An ideal alliance leader: Gone are the days when alliance management was a nascent business function and an after-thought. Today, as forward-looking businesses are leveraging their partners to tap into newer revenue streams, the art of managing alliances is now being recognized as a business acumen. Organizations are investing in these roles and are assigning Key Performance Indicators (KPIs) to generate partner-led revenue. Increasingly, alliance leaders directly report to the C-suite. Another clear trend is, often professionals who have spent a certain time in the industry and have taken up a variety of roles across geographies are hand-picked to don the mantle of an alliance leader. This can work well given that these individuals bring with them a well-rounded perspective of the industry and a vast professional network. This comes handy to diligently navigate both internal and partner organizations, align and advance. Better together: Businesses can radically improve their alliance success rates by incorporating the best practices. They need to invest in people, processes and relevant technologies to derive the full potential and future proof their alliances. It is both an art and a science. Therefore, it is important to build an alliance team, which is both diverse and inclusive. Such a team sparks new questions, challenges the status quo and fuels outperformance. The rewards of adopting alliance best practices can be big. The risks of not doing so may be even bigger.

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Trends Banks Can’t Ignore in 2022

Article | October 29, 2021

The competitive landscape of banking is changing rapidly. The non-traditional upstarts are threatening to disrupt the existing century-old business model in the financial industry. Banks have never experienced change, as we’ve seen in the last couple of years. Customers prefer Smart innovations like cryptocurrencies, peer-to-peer lending, smart contracts, new types of online security, and more. Some banks are adapting to meet these new challenges, while others are sticking to their old ways. The question is will they be successful, or will they be left out? And that is exactly what we would like to figure out with an in-depth and data-backed analysis. Based on this analysis we have answered the big Q in the industry- What do banks need to do this year to prepare for 2022 and beyond? Here is our take on trends banks can’t ignore in 2022. 1 Social Media Storytelling and Copywriting Contrary to popular belief, the real story of today’s customers is not all about you, your brand essence, or your products. But, it's more about how your customer truly feel about you when all is said and done. The fact is, customers are more sceptical than ever. Hence, banks need to examine their brand's essence to gain customer loyalty and trust in the digital world. Banks that are trusted by its customers will win the survival race in this digital age. Hence you need to create long-term relationship with your customers. For which you should create connections where in you : Provide insight about what your prospects and customers are experiencing and what you are offering. Explain how your offering is in their best interest rather than your own. Position your offer as the ultimate solution to your prospects and customers. Appeal to your prospect’s emotions by explaining how your product or service will improve their life. Present your offer as a complete package — think of it in a way that your prospective customers/clients will expect more from your brand. 2 Gen Z and its chronicles Gen Z is becoming the new consumer group; unlike previous generations, they are poles apart in several ways. Most importantly, Gen Z is tech-savvy and digitally sound. Gen Z grew up in a world that was moving into massive digitalization, immersed in technology and was taught to use computers early. They grew up understanding how technology can benefit their lives; they know how to use their phones and computers to stay in touch with their friends and use social media to stay updated with the latest information and breaking news. But, more than any other generation, Genz Z is concerned about their financial future. They have financially sound knowledge and are concious about their financial assets and investments from a very young age. It is this tech-savvy generation’s attention that banks need to address. Many Gen Z consumers are now coming of age in a time of uncertainty and instability. The financial crisis of 2008 and the ensuing Great Recession caused a financial panic, and many people lost their homes, their jobs, and even their savings. Gen Z, who grew up in this uncertain time, has come to view finance as an essential life skill. Therefore, they are more informed and educated about personal finance and far more willing to become financially savvy than previous generations. This is great news for banks and other financial institutions because it means that Gen Z consumers, more than any other generation, are willing to keep up with financial trends and seek out financial advice. As a result, banks can market their newest services like BNPL, to millennials. 3 Using CDP (Customer data platform) to target the perfect audience A customer data platform (CDP) is a platform that aggregates customer data produced by various channels and devices, and connects it with back-end systems. Companies may use CDPs to centrally store, manage, activate, and analyze consumer data. These platforms also provide connectivity with third-party solutions like Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), or Marketing Automation (MA), allowing marketers to use the CDP's capabilities while creating customer-centric strategies. Modern day marketers need to understand consumer behavior better, detect patterns, and customize customer experiences to connect with customers. A deep understanding on these metrics will help marketers reach their customers diligently, for which marketers need CDP. A CDP may also let marketers gather data from third-party websites or mobile apps. Across financial services and fintech, CDPs have the potential to revolutionize the way that consumers view and interact with their companies. A CDP, or customer data platform, gives marketers access to a centralized platform, or hub, containing a person’s data (including its identity), interests and preferences, and information about their interactions across the company’s many channels. Marketers can gather, analyze and act on this data, using many tools; to drive personalized experiences and communications. We're seeing technology advance and be commoditized in a way that we've never really seen before with the advancements around artificial intelligence and cloud capability, or even the revolution that we're seeing within the core banking sphere is really changing what financial services actually means.” -David Brear, CEO and Co-founder at 11:FS 4 Self Service We are living in the age of customers. Today, consumers expect greater accessibility and personalization. They want banking tailored to their needs. They want the ability to talk to humans when they need – but also, they want banking and payment tools that are intuitive and simple to use. Consumers also want to speed things up as much as possible. They want everything to be quick and convenient. They want banking to be personalized and contextual. As we look at the future of digital banking, we see consumers interacting in a much different way than they did just a few years ago. It’s no longer about meeting every expectation but about anticipating them. For exampleif you know that your customers usually travel on Tuesdays, you can suggest that them to pay their bills on their return. This way, you won’t miss a payment, and your customers won’t incur late fees. With more and more banks moving their infrastructure to the cloud, self-service banking is getting increasingly sophisticated. When we talk about self service we are not talking about ATMs or Digital wallets. We’re talking machine learning and artificial intelligence, cognitive technologies, and conversational interfaces. We’re talking about banking in 2022 and beyond. Its high time you analyse how your bank is performing in terms of the above-mentioned trends and start investing in them accordingly. 5 FAQs Q. What are the new challenges faced by modern banks? A. Today, customers demand "quick" access to their money, and regulators are concerned that banks aren't providing enough services. So, banks have responded by making greater use of credit cards, debit cards and other financial products. As banks have moved into other areas, they are faced with new challenges like: Increased competition. A cultural shift. Regulatory compliance. Changing business models. Rising expectations. Q. What are some trends in modern banking? A. The goal is to give customers a seamless and convenient digital banking experience. To stay competitive, banks are looking for ways to innovate. Some are turning to technology, including AI, machine learning, and automation. Others are adopting new strategies, like opening innovation labs and inviting outside entrepreneurs to test new products. Q. Can banks use the public cloud? A. Yes, banks can use the public cloud. , banks are taking note of the benefits of cloud computing. The cloud-based software-as-a-service model, for example, allows banks to focus on their core banking operations and outsource the management, maintenance and support of their IT infrastructure. Q. What is the future of banking? A. The Digital Revolution is changing the way people do their banking. Given the right access to the right information at the right time through digital means, customers are increasingly shifting transactions online. This has empowered them to make better financial decisions. { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "What are the new challenges faced by modern banks?", "acceptedAnswer": { "@type": "Answer", "text": "A. Today, customers demand \"quick\" access to their money, and regulators are concerned that banks aren't providing enough services. So, banks have responded by making greater use of credit cards, debit cards and other financial products. As banks have moved into other areas, they are faced with new challenges like: Increased competition. A cultural shift. Regulatory compliance. Changing business models. Rising expectations." } },{ "@type": "Question", "name": "What are some trends in modern banking?", "acceptedAnswer": { "@type": "Answer", "text": "The goal is to give customers a seamless and convenient digital banking experience. To stay competitive, banks are looking for ways to innovate. Some are turning to technology, including AI, machine learning, and automation. Others are adopting new strategies, like opening innovation labs and inviting outside entrepreneurs to test new products." } },{ "@type": "Question", "name": "Can banks use the public cloud?", "acceptedAnswer": { "@type": "Answer", "text": "Yes, banks can use the public cloud. , banks are taking note of the benefits of cloud computing. The cloud-based software-as-a-service model, for example, allows banks to focus on their core banking operations and outsource the management, maintenance and support of their IT infrastructure." } },{ "@type": "Question", "name": "What is the future of banking?", "acceptedAnswer": { "@type": "Answer", "text": "The Digital Revolution is changing the way people do their banking. Given the right access to the right information at the right time through digital means, customers are increasingly shifting transactions online. This has empowered them to make better financial decisions." } }] }

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BDO Unibank

BDO is a full-service universal bank in the Philippines. It provides a complete array of industry-leading products and services including Lending (corporate and consumer), Deposit-taking, Foreign Exchange, Brokering, Trust and Investments, Credit Cards, Corporate Cash Management, and Remittances in the Philippines. Through its local subsidiaries, the Bank offers Leasing and Financing, Investment Banking, Private Banking, Rural Banking, Life Insurance, Insurance Brokerage, and Stock Brokerage services.

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