Article | July 16, 2020
In this series, we share insights from our Citi Country Officers (CCOs) around the globe as they reflect on their experiences during COVID-19. CCOs are responsible for leading the entire Citi franchise in their country. They provide alignment and leadership to bring our global strategy to life in each of their jurisdictions. For us at Citi in Italy we prepared for the COVID pandemic in a number of different ways. First, we leveraged lessons learned from our colleagues in the countries that were hit before us, including China and Korea, even if their context was quite different from the European one.
Article | March 6, 2020
With barely six months remaining before the September 2020 deadline for European Commission’s implementing regulation 2018/1212 for Shareholder Rights Directive II (SRDII), firms operating as financial intermediaries are focused on planning for compliance. SRDII is aimed to improve corporate governance in EU member states. The European parliament published SRDII in May 2017 as an amending directive to 2007 SRDI, aimed to strengthen the position of shareholders and to improve shareholder influence on corporate governance and other factors in companies that are either traded in the EU’s regulated markets or have a registered office in any of the member states of EU.
Article | June 1, 2021
The axing of third-party cookies by Google and the other major browser companies will require a major readjustment by financial services organisations.
The decision, coming fully into force next year, will effectively choke off the data that has enabled personalisation, optimised website interactions and driven much internet advertising. It is no comfort that the browser companies have acted because of fears about infringement of privacy and data protection legislation such as the EU’s GDPR (General Data Protection Regulation) and the CCPA (California Consumer Privacy Act) in California.
The move will affect how UK financial services organisations interact with millions of people. More than three-quarters of Britons now use online banking and 14 million use digital-only banks, expecting a slick, light-touch interaction. So it appears that just as many people go digital, financial services organisations will no longer have access to information they need for personalisation, being unable to track where customers go on the internet after they have visited a bank’s website. All that data about individuals’ habits and preferences will be unavailable.
It seems catastrophic, but in reality, it is not. Financial organisations have a new opportunity to radically improve how they interact with web visitors and customers. AI-powered behavioural analytics offer far superior, real-time capabilities, using the data from the first-party cookies on their own website domains and where available, data from customers’ transaction histories.
The result is a solution that is faster, more accurate and responsive than conventional technology relying on cookie data owned and stored by third-party organisations. Instead of relying on such data for relatively rigid profiling and personalisation, behavioural analytics enables real-time interactions based on a more dynamic picture of how an individual’s requirements are changing.
The technology analyses all the browsing characteristics including time on site, speed of movement and page views, as well as more obvious features such as interest in specific products. Historical data added to the analysis includes what customers did on previous visits and the interval between those visits, establishing patterns where possible.
The flexible advantages of behavioural analytics hubs in financial services
Segmentation allows a bank to identify customers as soon as they arrive on its site, according to whether they are a new or existing customer. Their behaviour then indicates what they want. Knowing what customers are interested in is important. Customers visit financial services websites for a host of reasons – from seeking information, to opening accounts, exploring loans and mortgage offers, making or setting up new payments. They may also want advice about investments and savings, pensions or small business finance. Almost all of these requirements involve quite complex mental processes which financial organisations can influence while consumers are on their sites.
Collecting the data is not difficult – the skill is in making it actionable in an effective way, replicating the ability of a perceptive employee to read a customer’s state of mind. Banks can do this by setting up a behavioural analytics hub to understand what a customer’s behaviour means and how it can be optimised.
Using customised parameters, the hub will, for instance, trigger a screen notification that prompts the web visitor to fill in a form requesting an appointment. In the case of existing customers, the technology can correlate health insurance offers with spending on fitness, and, in general, savings and investment recommendations can be tailored to the client’s concerns or goals as revealed by their navigation of a bank’s website or mobile app.
Banks can set up analytics to see when consumers are behaving in a way that indicates they about to leave the website, allowing them to intervene with a notification that could include an offer. This provides a positive outcome and avoids the blanket use of offers that undermines profitability.
It is a more sophisticated and personalised approach that avoids annoying pop-ups or recommendations that fail to match individual preferences. As part of a single AI-powered segmentation platform, the technology enables banks to personalise marketing content in SMS messages and emails sent to consumers (who consent), which deliver far better results through precise targeting.
Solutions for last-mile interaction in the open banking era
The single platform approach also has another major advantage. It is much easier to implement and far more efficient and streamlined compared with separate solutions for different parts of the customer journey.
The benefits of using AI-powered segmentation solutions should be part of the financial sector’s broader strategy to transform its systems for the open banking era as we approach the end of third-party cookies. For established banks, the reality for some time has been that complexity of systems has undermined their ability to deliver a high-quality last mile. This they can now address without huge disruption or investment.
The alternative is for financial services organisations to become lost on an ocean of data, losing track of customers. Behavioural analytics will bring banks new insights into customers that surpass third-party cookie data, being actionable and accurate and in real time. To provide a streamlined and profitable experience for themselves and their millions of customers, banks must now employ the latest advances in AI-powered behavioural analytics.
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.