Article | April 15, 2020
Soon after, the World Health Organization declared the Novel Coronavirus outbreak an official pandemic, a sudden pandemonium busted across the globe. The COVID-19 crisis carries with it the potential equally disruptive economic fallout. It has brought a dominant shift in reality; in addition to the effects that the virus has had on our private lives, businesses in every industry are being forced to find innovative ways of operating on every level and the world of Fintech is no exception.
Investors began drawing out their money. The stock markets of the world collapsed. Central banks made off-cycle rate cuts and inserted liquidity to keep the economy running. The financial services are currently confronting challenges on multiple fronts: banks have reduced their opening hours serving a few customers at a time due to social distancing rules, putting additional stress on channels like telephone service, online banking, and social media.
Simultaneously, numbers of consumers are desperately trying to contact their bank with questions, concerns, issues, or to request appropriate solutions for their finances impacted by the fallout from the coronavirus. This is not the first or last global crisis. FinTech and banking companies of the 21st century need to be programmed to operate in such circumstances.
Being a fairly new sector, fintech companies are already well-positioned to adapt to a world that suddenly exists primarily online; most fintech platforms now exist in online formats anyhow. Still, the paradigm shift brought to the world by coronavirus is forcing the fintech sector to come up with creative solutions when it comes to supporting customers, building relationships, boosting company morale, and planning for the future in this unprecedented situation.
The behaviors that people can execute from their living room or from their den are going to grow, and behaviors that require face-to-face interaction or getting out into the community are going to diminish. What does that mean? You should have greater mobile apps and digital adoption in general. If I had been holding off on signing up for PayPal, for instance, I might just do that.
Nigel Morris, Managing Partner of QED Investors, Co-Founder of Capital One
“Fintech companies are probably some of the best-equipped organizations to take on this crisis.”
If you’re a Fintech, you’re probably no more than 10 years old and as a result, have built your backend and developmental stacks on cloud environments allowing for operations to continue anywhere. Also, many business-to-business (b2b) communication tools that FinTech companies have naturally adopted and made available to employees such as Slack, Teams, Skype have been more heavily leveraged. Legacy financial brands are more than likely finding this time to be a bit more difficult.
Paul Geiger, President, and Co-founder of Post-Trade Asset Management Firm Theorem Technologies
The coronavirus pandemic could be destructive for multiple companies, but it's also flashing a spotlight on the potential of fintech. It has the potential to revolutionize how consumers and businesses handle financial transactions. There will surely be hits and knocks along the way, but fintech can manage to make this transition a lot easier. Here’s how.
Table of Content:
- Moving Back to the Digital Infrastructure
- Utilizing Robotic Process Automation Software in Banking (Robo-Advisory)
- Acceleration of Cashless and Contactless Payment Methods
- Artificial Intelligence and Block chain Technology
- Fintech Firms Providing Free Technology During the Coronavirus Crisis
Moving Back to the Digital Infrastructure
As the number of cases ticks up in the U.S., many are choosing to go cashless to circumvent potential hygiene concerns around handling banknotes. Almost half of the world’s population is now practicing self-quarantine. Interaction between humans is limited. All the public gathering places are closed down. Businesses that rely on people coming in or the shops that are commonly crowded are now starting to close down.
The prevailing crisis will change our mindset and habits and make us re-evaluate our current way of doing things. All thanks to fintech, giving smaller communities a chance to keep their business open and operating. With digital payment options and online transactions, you can pretty much order any service, any product. You can even access your favorite types of entertainment online and all that without breaking the rules of self-isolation. Social distancing is the only effective method to fight the spread of coronavirus, and fintech has made this practice much more natural. Online services that we can pay for through fintech are now substantially covering every industry.
These digital services have become such an essential part of our lives, and we should be appreciative of how easy it is to quarantine these days. Ordering groceries, favorite food, watching the newest movies, stuffs delivered right to our door, all this is only possible, because of different financial technologies and digital payments that have made money transfer easy and secure.
Learn more: https://www.euronews.com/2020/03/19/next-day-delivery-the-challenges-for-retailers-during-the-coronavirus-pandemic
Utilizing Robotic Process Automation Software in Banking (Robo-Advisory)
Robo advisors, without human interference, provide automated support for all financial advisory services, such as trading, investment, portfolio rebalancing, and tax saving. It works with a pre-defined algorithm and analytics, calculating the best returns and plans for each individual as per his/her requirements and preferences.
The need for no-touch and low touch investment solutions, which can deliver more informed intelligent analytics back to the user, will be more important than ever. Robo advisors are the next level in the evolution of asset management and financial advice. For the banking industry, robotics outlines a unique and underutilized way to increase productivity while minimizing traditional repetitive and manual-labor-intensive processes. The growth of Robo advisory services is attributable to the fact that it enables low-cost services, scalability, cognitive advice, and next-generation user experience.
Though, for the next generation, virtual agents may be an acceptable help for financial matters. It is a fact that client advisors must fit with Robo-Advisors and artificial agents and a human-led technology-enabled model to a technology-led human-enabled business model in financial services.
Acceleration of Cashless and Contactless Payment Methods
The new coronavirus fears could be enough to introduce mobile and cashless payments to those who otherwise didn’t see the appeal.
Learn more: https://capital.report/blogs/how-open-banking-is-catalyzing-payments-change/8262
Before the outbreak, mobile payments in the U.S. had not come close to global adoption rates. In China, by contrast, more than 80% of consumers used mobile payments in 2019, according to management consultancy Bain. In the U.S., major mobile payments apps had adoption rates of less than 10%.
Major payment companies have already warned of the virus hitting U.S. spending. Visa, Mastercard, and PayPal have all cut guidance due to the coronavirus.
I think this is an opportunity for a move to digital. Zelle, PayPal, and online banking could see a boost. I believe this crisis will accelerate and move people to utilize all forms of digital financial services.
Peter Gordon, Executive Vice President and Head of Emerging Payments at U.S. Bank,
The mobile fintech payment service that facilitates easy online and oﬀ-line payments are reaching millions of people and can truly capture the under banked population.
The coronavirus crisis represents a huge opportunity to accelerate cashless and contactless payments. Retail and consumer payments are leading the way in the adoption of innovative payment capabilities aided by the growth in e-commerce and increased penetration of mobile phones. B2B opportunities are also gaining increased traction as a result of being more efficient and cost-effective. Some key trends in digital payments are:
This includes Near Field Communication (NFC) adoption, host card emulation and QR code generation for electronic interactions between consumers and retailers.
Adoption of payment hubs.
Banks are getting interested in looking at investing in harmonizing their payment infrastructure by moving to payment hubs. These hubs can process any form of payments irrespective of the origination channel.
This is transforming the retail funds' transfer process by providing electronic cash to anyone in the span of a few minutes. Peer to peer money transfers has been around for a while and have witnessed high growth.
Built on blockchain infrastructure this offers greater speed and efficiency of the transaction.
Rise of marketplace banks.
Challenger banks are being supported across geographies and given licenses to operate freely. Banks, by adopting open banking, has started exposing their APIs to third parties for large scale payments.
Omni channel offerings.
The framework of digital payments provides a seamless customer experience across channels leading to better transaction experience.
The future of payments was already revolutionizing with new entrants such as contactless payment, NFC enabled smart phones, cloud-based PoS, and digital wallets. The crisis will merely accelerate the desire to tackle the issues with these new banking technologies.
Artificial Intelligence and Block chain Technology
Artificial intelligence and machine learning built upon distributed datasets and shared information are used for dynamic and psychographic client segmentation based on behavior and automated investment services. The adoption of such technology will become pivotal for large financial institutions to manage their client base and investment portfolios efficiently. In fintech, blockchain finds application in areas like digital ID, customer authentication, insurance, to name a few.
Blockchain practitioners are experimenting with this technology to bring out new use cases and applications to solve the complicated issues in the fintech industry.
PWC’s study of financial services and fintech shows that about 77 percent of the financial services industry is planning to adopt blockchain by 2020. The blockchain sector in fintech has been intended to provide banking with a more seamless and efficient experience. We will soon see the process of cash to crypto and vice versa to become ubiquitous.
Blockchain asset management solutions are an alternative to offshore banking because there are substantial efficiency gains for cross-border transactions, for both private clients and businesses. Traditional banking and blockchain solutions will converge over the next decade with blockchain to become a commonly used technology in financial services.
Learn more: https://www.forbes.com/sites/jeffkauflin/2019/12/26/five-fintech-predictions-for-2020-according-to-kleiner-perkins/#40c46b7d3d5a
So, when we look at these phases of development in the fintech sector, 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.
Fintech Firms Providing Free Technology during the Coronavirus Crisis
Right now, you will be feeling the need for more reliability in software and information. Luckily, some fintech companies and teams are there to alleviate these issues. To help banks help their customers through these difficult times, many fintech providers are extending free, discounted, or accelerated deployment offers to financial institutions.
Business downturns are not uncommon. Sometimes there are unexpected circumstances. During these periods of change, the winners and losers will be defined by those firms able to make quick and decisive transformation reflecting the new environment. This is not just true in banking but in every industry. Fintech companies from across the world are all-in when it comes to coping with the current global health crisis.
The progressions in digital technology are making the crisis more bearable and are empowering businesses to keep working with access to key services (communication, payments, credit, collaboration, etc.) while enabling social distancing and helping to fight COVID-19. We expect digital technology to experience another boost during and after the crisis.
Article | April 15, 2020
There’s an ongoing debate as to whether new trends in machine learning are mere hype or are actually providing tangible business value and helping shape financial services pricing and offering strategies. A survey of about 200 global insurance professionals conducted by Earnix in 2017 showed that more than half of the respondents are using machine learning technologies in their business. At the same time, only 14% view machine learning as a core strategy that all areas of the company are encouraged to use.
Article | April 15, 2020
Going far away beyond conventional attack detection, advanced machine learning operations assist organizations to stay one step ahead of financial fraudsters.
We hear tons of stories about account takeovers and hacking also. How can financial institutions detect and mitigate these attacks?
The world of fraud prevention in banking institutions has always been supported by rules. Bankers and their engineers were uniting rules engines on the banking data system to stop or identify common fraud patterns. For quite a while, this was sufficient. But today we are experiencing a change of society, a digital and technological revolution. Following the primary iPhone, and therefore the later mobile internet explosion, people are interconnected all the time, everywhere and for all quite useful. In this digital age, the digitization of means and behaviors forces corporations to revise their business model. As a result, banking institutions are going massively online and digital-first. Both the bank users and customers have unfolded their behaviors with the brand-new means offered by the digital era.
Learn more: https://deck7.io/Women-Leadership-verrency-audrey-blackmon
With the shift towards universal digitalization, perpetrators are finding new weak spots in financial digital applications. Ironically, the technology works both ways: it accommodates firms to supply more reliable customer experience and optimize operations and, at an equivalent time, aids cybercriminals in performing numerous sophisticated unlawful schemes.
According to the Association of Certified Fraud Examiners (ACFE), 30pc of fraud occurrences happened in small businesses, and 60pc of small-business fraud victims did not retrieve any of their losses.
According to Statista, in 2017, the global FDP (fraud detection and prevention) market was calculated to be worth $16.6 billion.
According to McKinsey, worldwide losses from card fraud could be close to $44 billion by 2025.
Financial crimes do not limit crimes like credit card fraud, tax dodging, and elder abuse. In fact, it includes much broader offenses – such as Identity theft, human trafficking, phishing, pharming, drug trafficking, money laundering, and terrorist financing that can have enduring impacts on society.
Fighting financial fraud is difficult because fraudsters frequently change and adapt. The moment you figure out how to identify and prevent one scam, a unique one emerges to take its spot. Identifying, eliminating, and blocking these threats are sensitive points for e-commerce and banking industries. Sincerely, the best technology for combating fraud is one that can evolve and adapt as instantly as the fraudster’s tactics. That’s what makes machine learning (ML) systems ideal to fight fraud and financial crime.
The big problem is that companies think they need to establish rules, policies, and procedures to prevent fraud. But today’s criminals are much more sophisticated and are able to circumvent these business rules. Businesses need to take a more dynamic approach that includes business rules as well as machine learning and AI to learn from evolving criminal behavior and deliver a more sophisticated and effective approach to dealing with financial crimes.
Andrew Simpson, Chief Operating Officer of CaseWare Analytics.
Why use machine learning to combat financial fraud?
Machine Learning knocks down the conventional ways of detecting fraud. It’s quicker, works with extensive amounts of data, and doesn’t rely on human resources. When designed optimally, it absorbs, adapts, and uncovers emerging patterns without the over-adaptation resulting in too many false positives. It’s time for ML to conclusively take center stage in assisting firms to recognize and counter fraud as fast as it’s performed.
How Machine Learning Helps in Fighting Fraud and Financial Crime?
Machine learning can learn normal behavior from training data and recognize abnormal behaviors that indicates money laundering, like, when money is transferred between suspicious geographies, active movement of funds between different accounts, or invoicing number sequences have been falsified. Machine learning is continually learning, and so they can recognize when the pattern of laundering change and adjust rapidly.
Analyzing Huge Amounts of Transaction Data
One of the most powerful features of machine learning algorithms is that they can analyze huge numbers of transaction data and flag suspicious transactions with highly accurate risk scores in real-time. Its algorithms serve 24/7 and process an immense amount of information with the flip of a switch. This risky analytics method recognizes complex patterns that are challenging for analysts to identify; this means banks and financial organizations are far more operationally proficient while detecting more fraud.
The algorithms take various factors into account, including; customer’s location, the device used, and other circumstantial data points to form a detailed picture of every transaction. This strategy improves real-time decisions and protects customers against fraud, all without affecting the user experience.
Thanks to extensive technological development, organizations will frequently rely on machine learning algorithms to determine which transactions are suspicious.
Learn more: https://www.sas.com/en_in/insights/articles/risk-fraud/strategies-fraud-detection.html#/
Supervised and Unsupervised Learning for Detecting Complex Patterns
Machines can be programmed to self-learn in an unsupervised model with ML so that transactions that do not conform to a set pattern are recognized and hence can be actioned upon in right period.
Machine Learning automatizes the extraction of aware and unaware patterns from data. Once it identifies those patterns, it can employ what it learns to new and unseen data. The machine learns and modifies as new outcomes and new patterns are introduced to it via a feedback loop.
In fraud detection, supervised machine learning algorithms can self-learn from targets within the data. While training a supervised model, it's important to present to it both fraudulent and non-fraudulent records that have been labeled as such.
Unsupervised Machine Learning is different. It reveals potentially unusual risks you might not watch for because it works without a target. Instead, it looks for irregularities in the data.
Machine Learning in Fraud Detection
The fraud detection method employing machine learning starts with gathering and segmenting the data. Alongside this, the machine learning model receives training sets that train it to predict the possibility of fraud. Conclusively, it creates a fraud detection model:
Input data- The first step is data input, which differs in Machine learning and humans. Humans strive to comprehend massive amounts of data, such a task is a five-finger play for ML. The more data an ML model eats, the better it can learn and polish its fraud detection abilities.
Extract Features- Extracted features defining good customer behavior and deceitful behavior are added. These features normally include the customer’s location, identity, orders, network, and preferred payment method. Based on the complexity of the fraud detection system, the list of examined features can vary.
Train Algorithm- Further in this process, a training algorithm is launched. In short, this algorithm is a collection of rules that a machine learning model has to pursue when deciding whether an operation is genuine or fraudulent. The more data a business can supply for a training set, the more reliable the ML model will be.
Create Model- After the training is over, an organization receives a fraud detection model acceptable for their business. This model can detect fraud in no time with great accuracy. To be efficient in credit card fraud detection, a machine learning model needs to be continually improved and updated. Eventually, fraudsters will turn up with new bamboozle to game the system unless you keep it updated.
Employing advanced fraud protection and detection systems electrified by ML, multiple industries can keep their finances secured.
Capgemini alleges their ML fraud detection system can lessen fraud investigation time by 70% while boosting accuracy by 90%.
Another ML fraud prevention solution provider, Feedzai, alleges that a well-trained machine learning solution can recognize and prevent 95% of all fraud while reducing the amount of human labor needed during the investigation stage.
Reduction of False Positives
With the level of complicatedness in today’s financial infrastructures, the term ‘false positive’ has become nearly correlated with the industry’s efforts to fight fraud. One of the banking’s most significant challenges is to minimize the number of false positives being generated, thereby saving time, money, and bypassing needlessly frustrating customers.
AI and machine learning play a significant role in this area. Because they are proficient in examining a much more comprehensive set of data points, connections between entities and fraud patterns – including fraud scenarios not yet known to fraud analysts – the predominance of false positives can be extremely reduced.
Bringing it all Together
Multinationals like Airbnb, Yelp, and Jet.com are already employing AI solutions to get insights from big data and counter issues such as fake accounts, account takeover, payment fraud, and promotion abuse. Machine learning entertains all the messy work of data analysis and predictive analytics and empowers companies to grow and develop secure from financial fraud and crime.
As mentioned, machine learning can be very convenient when it comes to fighting cybercrimes. ML prevents critical attacks on users’ and companies’ finances. It’s a quick, up-to-date, and cost-effective method to shield customers and the company’s data.
Article | April 15, 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.