Article | April 15, 2020
The 21st century doesn’t fail to surprise human society with its innovation. Blockchain is going as a part of mainstream business operations and it's impossible to keep FinTech unaffected.
As the new universality of the twenty-first century, technological advancements have now unquestionably seeped into the workflow structure across multiple industries and are an indispensable element of varied business processes. Assignments that once needed human hands, bulky machines, and physical currencies have now been efficiently digitized. Mobility, cloud services, and consumers who have grown up in the digital age are forcing a CHANGE.
Technology has been the core of a number of disruptive trends in financial services and it is a driver behind three key themes; the first being convergence of other industries into financial services that is frankly leveraging data and technology, the second is wholesale sort of interruption or disruption of business models and new entrants entering into the competitive landscape and certainly last is a much more transformative journey and that is the leveraging of things like Blockchain Technology, which is completely going to change the financial services ecosystem and marketplace in 2020.
Anyone with an internet connection can now engage in day-to-day banking activities, trading and investment in the stock market, widen e-commerce platforms, make online payments, exchange currency online, undertake equity funding, and more. Similarly, new players are now experimenting in different areas of financial activity such as banking, payments, peer-to-peer lending, wealth management, and more.
FinTech itself is at the cusp of the renovation as if there was a need. That flux of change is coming from the headwinds of Blockchain swinging its wings. Its stagnant style of doing business is apparent to all. What needs to be examined though, in this distinct phenomenon is the contribution of Blockchain which has enhanced this progressive revolution.
Table of Contents
•Why use Blockchain for Fintech?
•How is it currently managed?
•How big is the impact of Blockchain in FinTech?
•Blockchain for Global Payments/Cross-Border Transfers
•Blockchain in Trading and Trade Finance
•Blockchain in e-KYC Utilities
•for Credit Scoring
•Conclusion (All in all)
Why use Blockchain for Fintech?
When we talk about FinTech, or technology for finance, we are going to touch a very delicate aspect. We are in 2020 and banks still demand people to send them a fax with their information, because regulators that are there are not catching up with the technology. So Blockchain for FinTech is a very powerful tool. But until the regulators don’t allow it to be deployed in full, recognizing digital signature, recognizing a contract, a time stamp by blocks in a blockchain is going to be hard.
How is it currently managed?
Let’s understand this with an example - payment with a credit card. Before the payment with a credit card arrives in our bank account, there are 12 companies with 12 databases. They bring the data one to the other before it arrives to the bank account. With the blockchain, it’s up in a single transaction. So in many cases for remittance and for rebalancing accounts, even between branches of a single bank, a blockchain solution allows to remove error in transaction. There are banks that have branches in different time zones. At the end of the month, one time zone branch writes the transaction in the previous month, the headquarters writes the transaction in the next month. And when the month goes to level, it creates a lot of confusion. The accounting system based on blockchain technology will guarantee that all is aligned perfectly.
How big is the impact of Blockchain in FinTech?
Blockchain surely is born for fintech and is already bringing quite a lot of interest. The reaction of the financial industry is being very positive, one of adoption. When the financial board saw blockchain, rather than getting scared, they started adopting the technology for their own good. In fintech, blockchain is making a big influence to start with.
According to a survey on the financial services sector and fintech conducted by PwC, around 77% of the financial services industry plan on adopting blockchain by 2020. Banks being 1/3rd of the institutions surveyed have shown an inclination in incorporating blockchain in their operations as was reported by a study published by Accenture and McLagan (January 2017) that made mention of at least eight of the ten biggest global investment banks comprising the blockchain route.
Blockchain for Global Payments/Cross-Border Transfers
Blockchain-powered payments are hyper-secure and private. Each user has personal cryptocurrency keys that they can use to conduct transactions safely. The blockchain ensures that only participants involved in a particular transaction know the details of this transaction. Any changes to the transaction are possible only with the consent of all participants.
Learn more: https://capital.report/blogs/tracking-the-future-of-cross-border-payments-with-ai-ml-and-blockchain/8124
As per Deloitte, blockchain-based payments from business-to-business and peer-to-peer results in 40% - 80% reduced transaction costs. They’re also settled within seconds. Yes, it would be a paradigm shift but as per a projection by Mckinsey & Co. blockchain could drive $50 - $60 Billion in transcontinental B2B and $3 - $5 Billion in P2P payments respectively.
A blockchain records and validates every transaction and administers transactions in a way that no one can tamper with or delete them post-execution. FinTech companies such as Aeternity leverage this advantage of the blockchain to protect payments.
Another benefit of blockchain is that it eradicates the need for a mediator to handle financial services like money transfers. This is a huge relief for businesses that provide peer-to-peer (P2P) transactions.
Learn more: https://rubygarage.org/blog/how-blockchain-works#article_title_1
Blockchain in Trading and Trade Finance
The trade financing field requires lots of tedious paperwork and bureaucracy. Stock and share purchases have to pass through brokers, exchanges, clearing, and settlement. Shipping, for example, requires client-side etiquettes like lading bills, invoices, and the letter of credit. Each transaction is typically completed within three days. Yet transactions can be delayed when trading transpires over the weekends.
The blockchain technology can release traders from troublesome checks of counterparties and optimize the complete lifecycle of a trade. Using a blockchain, companies can intensify trade accuracy, speed up the settlement process, and reduce contingencies.
Ornua and Barclays completed the world’s first blockchain trade transactions in 2016, employing four hours rather than a week on a letter of credit — a document guaranteeing the export of $100,000 worth of agricultural products. IBM & Maersk collaborated for a global trade platform to attain scalable solutions of Blockchain in Fintech. Furthermore, Forbes released its report of Top 50 Billion-Dollar companies who’re exploring the scope of implementing blockchain solutions.
Learn more: https://www.forbes.com/sites/michaeldelcastillo/2019/04/16/blockchain-50-billion-dollar-babies/#3d2cf7be57cc
Blockchain in e-KYC Utilities
Identity can be undoubtedly established by government-issued documents such as driver‘s licenses, social security cards or passports, etc. Establishing identity through KYC verification is a lengthy procedure.
While exploring the bank-driven approach to KYC customer record sharing, there is always a debate around centralized versus decentralized approaches.
According to Niall Twomey, Chief Technical Officer, Fenergo, The centralized model offered centralized KYC utilities, controlled by a single entity. The main proponents and vendors behind these models at the time were incumbents with huge data resources and reach, which makes sense when it comes to creating a KYC utility. However, each utility had separate financial institution members, meaning that the overlap of customers and the ability to re-use customer information between them was seriously diluted. This was a key showstopper for utilities at the time. This led to a shift towards a decentralized model, where control is shared and participants coordinate with each other without going through a single intermediary.
Blockchain is a form of distributed ledger technology, having a specific technological foundation and cryptographic features that enable the storage of data in an immutable (unchangeable) ledger of ‘blocks’ of records. The blocks of records are linked in groups or a ‘chain’, which are maintained by a decentralized network, where all records are approved by consensus. It can build trust between financial institutions as it is auditable, and can help streamline the attestation process; ensuring clients are in charge of their own personally identifiable data.
The use of blockchain, currently best known as the foundational technology for Bitcoin and other cryptocurrencies, could overcome inefficiencies and duplication of effort in KYC information gathering between legal entities within a more comprehensive financial corporation or even between competing banks.
The blockchain offers a digital identity system. Using this system, clients need to go through validation just once and can then use this verified identity document to conduct transactions all over the world. A blockchain allows clients to
• Manage their personal identity data and reputation;
• Share their data with others without safety concerns;
• Log in to digital services without passwords;
• Digitally sign any type of documents, such as claims and transactions.
for Credit Scoring
FinTech companies are widely using blockchain to cater to the unbanked population lacking CIBIL score and helping them get credit. Apart from the unbanked and underbanked, two more groups of consumers — credit invisible and unscorable — lack banking services. The Consumer Financial Protection Bureau (CFPB) shows that one in ten adults in the USA don’t have any credit history, and 19 million Americans have unscored credit records.
Unscorable consumers mean people who have credit records at least in one credit reference agency but the data is too out-of-date to generate a reliable score. Consequently, millions of people are deprived of loans, mortgages, the ability to rent apartments, and more.
Traditional banks and lenders approve loans based on a system of credit reporting. Blockchain technology unlocks the possibility of peer-to-peer loans, complex programmed loans that can approximate a mortgage or syndicated loan structure, and a faster and more secure loan process in general.
When you apply for a bank loan, the bank evaluates the risk involved. They do this by looking at factors like your credit score, debt-to-income ratio, and homeownership status. This centralized system is often unfriendly to consumers. The Federal Trade Commission concludes that one in five Americans have a “potentially material error” in their credit score that negatively affects their ability to get a loan
Alternative lending using blockchain technology offers a cheaper, more efficient, and more secure way of making personal loans to a broader pool of consumers. With a cryptographically secure, decentralized registry of historical payments, consumers could apply for loans based on a global credit score.
All in all
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 repetitive and complicated issues in the fintech industry.
Blockchain in fintech is anticipated to reach $6,700 million by 2023 in the United States. Financial institutions will use blockchain for smart contracts, digital payments, identity management, and trading shares. 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 technology has tremendous potential to deliver excellence in core areas of banking and financial institutions’ business model. But to succeed in implementing blockchain, financial institutions should collaborate with the ecosystem before they launch blockchain solutions.
Article | February 11, 2020
The pace of change within the global payment’s technology space is still at full speed with no sign of slowing down. While traditional incumbents have until recently taken comfort in their size and decades of dominance, new digital-only challenger banks are ramping up and making a huge impact on the global financial landscape.
Article | February 25, 2020
The overhaul of the financial services industry in the post Great Financial Crisis (GFC) era has created a swathe of new rules and regulations for the regulators to monitor, and financial firms to adhere to. While such changes have presented firms with significant challenges, transforming how they operate and behave on a day-to-day basis, it has coincided with another notable overhaul in the tech arena.
Article | April 20, 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.