How Machine Learning Will Transform P2P Lending

artificial intelligence machine learning p2p lending

Artificial Intelligence, fondly known as AI, is the approach and research field of creating machines that are able to replicate human intelligence. Although we aren’t yet teeming with robots as intelligent as humans, we have managed to build: Intelligent programs that can beat reigning Go world champs Virtual assistants like Siri and Google Assistant to […]

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artificial intelligence machine learning p2p lending

Artificial Intelligence, fondly known as AI, is the approach and research field of creating machines that are able to replicate human intelligence.

Although we aren’t yet teeming with robots as intelligent as humans, we have managed to build:

With the passing of time, more and more fields are benefiting from AI and machine learning (ML). The latter is a specific form of AI that deals with devising strategies and methods for allowing a machine to learn from the available and historical data, all to find better solutions for existing problems.

Among the various markets growing by leaps and bounds with machine learning is marketplace lending. AI and machine learning have enabled this sector to automate a variety of processes, shrink operating costs, and make life easier for both lenders and borrowers.

In the past two decades, there has been a tremendous surge in people interested in learning ML as a hobby as well as a professional career choice. Before diving deeper into the subject of how machine learning will transform P2P lending in the future, let’s first get ourselves a brief understanding of ML.

The Craft of Machine Learning a.k.a. ML

Machine learning, or ML, is a subfield of artificial intelligence that follows the notion that machines can learn and adapt better from experience rather than from extensive programming.

Although machine learning is a subset of AI, there are several notable differences between AI and ML. Hence, both fields are considered to be distinct. This analogy is followed by deep learning and machine learning too, where the former is the subfield of the latter.

Today, we are surrounded by many instances of ML, varying from Apple’s Face ID and facial recognition in general to navigational and shopping recommendations generated by virtual assistants. ML also had a profound impact on the financial industry, especially the lending sector, which we are going to discuss in the upcoming sections.

The Present Impact of Machine Learning on P2P Lending

The ongoing evolution of technology and the emergence of tech innovation is having a greater impact on the financial services industry. From designing impactful business models and enhancing the customer experience to cutting costs, the benefits are many.

Fintech firms and the Internet are providing platforms to even niche products to get interested financial supporters as well as potential customers to grab their side. Peer-to-peer financing isn’t only about crowdfunding, but also includes P2P lending.

A bank offers P2P lending by allocating capital from the lending party to the borrowing party while acting as the broker, as well as the risk mitigator. The higher the risk, the higher is the imposed interest rate. Fintech organizations have made this simpler and more effective.

Several Fintech P2P lending firms like LendingClub and Prosper offer a platform where individuals gain interest by lending money to the ones who require it. These firms make money by taking a small fee for making the connection among the two parties possible.

As an alternative to submitting a traditional application, borrowers present compelling stories about why they need the capital via fintech platforms.

Other than the aforementioned, here are some of the most important benefits presently enjoyed by the P2P lending industry thanks to adding the art of machine learning to its arsenal:

1. Easy Identification of Defaulters

As the financial sector grows, so does the number of defaulters and bad loans. This has led the financial institutions to be more careful than ever to spot and avoid defaulters.

Machine learning, with its predictive analysis, can help lenders identify early the borrowers who are highly likely to default later. An early deduction of defaulters can significantly lessen the credit risks, which, in turn, reduces the total number of actual defaulter cases.

2. Hastening the Loaning Process

Of course, lending is one of the most complex business processes. This is the prominent reason why it is a long and tiresome process to accomplish. Thanks to its intuitive analysis, machine learning can remove the redundant parts and, hence, speed up the whole process.

An automated workflow offers lenders a competitive edge against the competition as there is less space for human error. Thus, lenders can process mammoth workloads in shorter time periods. This helps them not only to maximize profit, but also to better serve their clientele.

3. Lessening/Eradicating Errors

Lending involves a lot of documentation. From the very early stage of loan origination to underwriting, most of the steps involved demand preparing and verifying several documents. Doing it manually not only increases the overall time required but also makes it prone to errors.

Machine learning comes in handy here by adding automation to the process, prompting for human intervention where required the most, and thus, streamlining the complete loan cycle.

4. Reducing Operating Costs by Automating Several Aspects of the Lending Process

There are several aspects contributing to the operating costs of a financial institution. Among them, the most notable aspect is loan decisioning. In most cases, the loan arrives at a positive lending decision irrespective of the loan amount.

Machine learning is able to automate the process, consolidate the entire data, and process the same by taking into account a number of factors. Moreover, ML performs a credit check and enhances the overall experience and quality of the process while reducing the overall costs.

5. Simplifying the Whole Process

The traditional way of lending is a very complex process. Hence, it is obvious for lenders to look for options that can simplify the conventional lending workflow (i.e. decreasing the total time required, lowering the overall cost involved, and smoothing out the whole process).

Machine learning helps in simplifying the whole lending process by:

  • Automating various aspects of the business process with customizable rules engine
  • Easing the borrowing process
  • Ensuring straightforward processing of loan applications
  • Standardizing the entire process

The Upcoming Impact of Machine Learning on P2P Lending

With time, artificial intelligence and machine learning are getting bigger and better. This is evident by the fact that there’s a continuous rise in candidates interested in learning AI in general, and ML in particular.

Although machine learning has already started revolutionizing the way P2P lending works, it will continue getting better and more beneficial for the craft. Following are some projections about how ML will impact lending in the near future:

  • Ability to self-modify with little to no requirement for human intervention
  • Better means of filtering irrelevant data from relevant data
  • Completely automating the underwriting process
  • Even faster application, approval, and funding processes
  • Helping lenders to see the bigger picture by insights drawn using efficient ML algorithms
  • Increasing the outreach of lenders and identifying potential lenders
  • Introducing younger people to the lending-borrowing culture
  • More stringent safety protocols to safeguard sensitive information
  • Offering insightful decisions with a higher probability of success
  • Providing capital to more and more honest borrowers
  • Recognize patterns in a specific dataset to help build effective business models
  • Using more number of data points to better check the likelihood of the borrower repayment and estimating the time period of the lender getting the amount back

Challenges Posed by Machine Learning Algorithms

Although machine learning provides access to a variety of avenues for financial institutions, it still needs to do the heavy lifting in some areas to further improve. Three of the most important aspects of ML requiring effort are discussed as follows:

1. Concealed and Undesired Bias

Bias is an inseparable aspect of machine learning algorithms. No ML algorithm can exist bias-free. Although a small degree of bias can be easily managed, the bigger it becomes, the more difficult it gets to fix the same.

Chances for machine learning algorithms to develop concealed and undesired biases from the same data used to train them do exist. Hence, human intervention becomes absolutely necessary to ensure that apposite suggestions or predictions are made by the ML systems.

2. Undetected Errors

Machine learning aims to minimize human interference as much as possible. Hence, it kind of makes humans dependent on machines. Most times, they are free from errors. However, there is room for some errors to slip through.

In case of errors that the machines aren’t able to correct themselves, human intervention becomes mandatory. However, manually identifying and correcting complex algorithms can be arduous and time-consuming.

As lending is an important aspect of the economy, even the smallest of errors being unintentionally passed through can propagate through the system and lead to a huge, undesirable consequence.

3. Time Requirement for Developing Precise Predictions

Machine learning algorithms rely greatly on historical data. The accuracy of the predictions developed by ML algorithms is directly proportional to the total time they spend interacting with the data. Therefore, making precise predictions instantly isn’t possible.

It is mandatory to feed historical data as well as the new data to the ML system continuously for ensuring that the predictions produced by the same are accurate and reliable.

Afternote

There is no denying the fact that the advent of artificial intelligence and machine learning has completely altered the way the lending sector works today. It has become faster, bigger, and better than ever with continuous improvement.

Borrowers and lenders, irrespective of the capital involved, aim to have as few formalities as possible. Though it seems unachievable with the traditional form of lending, the technology-backed lending strives to move as much closer to it as possible.

Fintech firms leverage the latest technological innovations to offer a quicker, smoother, and reliable experience to the lenders and borrowers. There are lots of challenges faced by the industry as of now. Developing better machine learning algorithms might be the key to solving them all.

The post How Machine Learning Will Transform P2P Lending appeared first on Lending Times.

Friday March 30 2018, Daily News Digest

Friday March 30 2018, Daily News Digest

News Comments Today’s main news: LendingClub grows short interest. Larry Summers resigns from LendingClub board. RealtyShares intros gap financing for projects under $20M. Mark Davies steps down from RateSetter board. Today’s main analysis: Heap’s behavior attribution platform. Today’s thought-provoking articles: Can Noto sell mortgages at his new SoFi post? China banks report drop in bad loans. Chinese families rack up […]

Friday March 30 2018, Daily News Digest

News Comments

United States

United Kingdom

China

Other

News Summary

United States

LendingClub Corp (LC) Sees Significant Growth in Short Interest (the Ledger Gazette), Rated: AAA

LendingClub Corp (NYSE:LC) was the recipient of a large increase in short interest during the month of February. As of February 28th, there was short interest totalling 31,244,316 shares, an increase of 11.0% from the February 15th total of 28,142,392 shares. Based on an average daily volume of 9,132,224 shares, the days-to-cover ratio is presently 3.4 days. Currently, 11.3% of the company’s stock are short sold.

Larry Summers Has Resigned From the LendingClub Board (Lend Academy), Rated: AAA

Yesterday, we learned that after nearly six years Mr. Summers will be leaving the board of LendingClub. He is being replaced by leading economist and Stanford professor Susan Athey. While she is not nearly as well known as Larry Summers she still brings serious economics clout to the board.

Heap launches behavioral attribution platform (Venture Beat), Rated: AAA

Source Heap

Heap is aiming to automate insights and is starting today with the launch of Heap Behavior Attribution. The new product is the industry’s first attribution product that measures behavior and does so in a way that requires no data science or engineering resources, the company said.

The Heap Behavioral Attribution measures standard marketing channels (i.e. Google and Facebook), and also ties in a set of broad user behavior, including email, customer relationship management (CRM), shopping cart, customer success, and either-or testing platforms. Examples include user behaviors stored in Salesforce, Marketo, Shopify, Autopilot, Optimizely, Oracle, and more.

Source Heap

It has more than 100 employees and 6,000 customers, including Twilio, Lending Club, App Annie, Morningstar, Monotype, and Casper.

 

RealtyShares Introduces Gap Financing Program For Projects Under $ 20 Million (Business Wire), Rated: AAA

RealtyShares today announced a gap financing program that delivers subordinated financing solutions to commercial real estate owners seeking higher leverage on the financing of projects under $20 million. The suite of solutions, which includes preferred equity, mezzanine debt, and second lien loans, helps commercial real estate operators get the capital they need to buy, refinance, or renovate commercial properties.

He Can Fix Your Student Debt, but Can He Sell You a Mortgage? (Bloomberg), Rated: AAA

As chief operating officer of Twitter Inc., Anthony Noto did a lot to calm the company’s perpetually anxious shareholders. On Feb. 26, however, Noto took over as chief executive officer of a financial technology startup, Social Finance Inc., or SoFi.

He’ll be facing increasingly tough competition. SoFi sees Marcus, the consumer-lending business started by Goldman Sachs Group Inc. in 2016, as the biggest threat, according to people familiar with SoFi’s thinking.

One question Noto will have to navigate is how much SoFi should use its own balance sheet—that is, hold on to the loans it originates as opposed to selling them to other investors. It currently keeps a slice of loans but sells off most of them. Holding loans allows a company to earn a stream of interest income, but investors generally put a lower value on financial firms than tech platforms.

SoFi says it plans to hold 500 events in 2018, up from 41 in 2015.

The wealth management unit, fully launched in May 2017, had $42.3 million in assets under management as of Jan. 18, according to Prosser.

 

Instant Financing Offers Drive Sales, Decrease Cart Abandonment (Retail Touchpoints), Rated: AAA

E-Commerce retailers in the U.S. recognize the value of instant financing offers, and 64% believe providing financing options through their online store is important to driving new and increased sales, according to a survey from Klarna. Another 46% believe such services decrease cart abandonment, which is a pressing concern for retailers.

Instant financing is a revolving line of credit that shoppers can apply for during online checkout, letting them spread payments out over time with low annual percentage rate (APR) offers. The option is particularly appealing to Millennials, as fewer than 33% of them carry credit cards, according to a 2016 Bankrate survey.

Retailers’ enthusiasm for online financing is shared by shoppers, and a 2017 Researchscape International survey conducted on behalf of Klarna found that consumers:

  • Prefer online merchants that offer instant financing (75%);
  • Want to be presented with an instant financing option when shopping online (47%);
  • Would spend more if given instant credit options when making a purchase (39%); and
  • Are very or completely likely to change retailers to use instant financing (28%).

FINANCE THEORY, LISTED EQUITIES, AND LIQUIDITY (AllAboutAlpha), Rated: A

A recent paper from Robeco discusses whether a liquidity premium exists in the stock market. The authors, David Blitz, Jean-Paul van Brakel, and Milan Vidojevic, conclude that “the evidence for such a premium is, at best, weak.”

Less politely, these authors refer to the whole notion of a liquidity premium as having been “challenged and debunked in various studies.”

Theory and Practice

In a sense there “should” be a liquidity premium. The more illiquid a stock, the more difficult it is to trade it, which on some models means that illiquid stocks are less attractive than liquid stocks, and should command a premium. One should have to be bribed to hold an illiquid stock just as one has to be bribed to hold a risky one.

BLACK ENTREPRENEURS, SMALL BUSINESS OWNERS NOT TAKING ADVANTAGE OF SBA OFFERINGS (Black Enterprise), Rated: A

Here is a surprising statistic: Over 80% of small business owners have never visited their local U.S. Small Business Administration office.

The finding was revealed as part of a random survey of 409 owners and senior leadership at small businesses by national online lender Fundera and online research firm Qualtrics.

Fundera also offers a list of other resources black entrepreneurs can tap into to help them get help with everything from how to run a small business to becoming a certified minority business.

Marketplace Lending Update #2: Another Rocky Mountain Remand (The National Law Review), Rated: A

In our prior Clients & Friends Memo “Who’s My Lender?” published on March 14, 2018, we analyzed two actions brought against marketplace lenders, one against Kabbage Inc. (“Kabbage”) in federal court in Massachusetts1 and the other against Avant in federal court in Colorado.2 In that memo, we noted that the Massachusetts action against Kabbage is proceeding to arbitration, while the action against Avant was remanded to state court.

Last week, Colorado courts issued several new rulings related to marketplace lending. First, the federal court in Colorado remanded another enforcement action brought by the Administrator of the Colorado Consumer Credit Code against Marlette Funding (“Marlette”),3 which had been doing business as a marketplace lender in Colorado under the name Best Egg. Following the reasoning in the Avant decision discussed in our prior memo, the court rejected the marketplace lender’s argument that Colorado’s usury laws were subject to complete preemption under federal law and therefore the court granted plaintiff’s motion to remand. As a result, Avant and Marlette will be forced to make their arguments that a bank is the “true lender” and that the Colorado Administrator’s usury claims are therefore preempted by federal law, and any other defenses, in Colorado state court.

KBRA Comments on Cross River Bank’s Settlement with the FDIC (Business Wire), Rated: A

The FDIC announced yesterday that it had reached settlements with Cross River Bank (“Cross River”) and Freedom Financial Asset Management (FFAM). Kroll Bond Rating Agency (KBRA) believes that the settlement and related consent order have a low likelihood of adverse impact upon the credit profile of Cross River and that of its parent, CRB Group, Inc. (CRB). While FFAM represents a very small portion of Cross River’s customer base, KBRA believes any adverse regulatory action draws heightened scrutiny to Cross River and the MPL industry, a factor already considered in the current ratings. Furthermore, KBRA believes that the matters cited by the FDIC were isolated instances and is not representative of pervasive issues with Cross River’s Compliance Management System (CMS). Nonetheless, we believe that has Cross River has since adopted enhanced compliance and reporting requirements consistent with FDIC guidance and incorporated enhancements to their CMS.

Enacomm and VOX Network Solutions to Provide Financial Institutions with Data-Driven Phone and Digital Assistant Banking (Global Newswire), Rated: B

VOX Network Solutions (VOX) has announced a partnership with Enacomm, Inc. (Enacomm) to bring Enacomm’s self-service solutions to VOX clients.  Through the reseller agreement, financial institutions will be equipped with VPA (Virtual Personal Assistant) banking and the Enacomm Financial Suite (EFS), which includes a hosted, dynamic interactive voice response (IVR) system for personalized customer interactions.

Crypto Asset Expert David Drake Joins Advisory Board of Digits (Nothing in Particular Blog), Rated: B

Digits, a leading crypto company using technology aimed to combine the convenience of credit and debit card payments with the utility of cryptocurrency payments and to easily allow the consumer the ability to effortlessly pay for goods and services with crypto via their existing credit or debit card, announced today the addition of a highly respected crypto expert, David Drake, to its advisory board team.

United Kingdom

Betfair founder Mark Davies steps down from RateSetter board (Peer2Peer Finance), Rated: AAA

MARK Davies has stepped down from the board of RateSetter after more than six years.

Davies, who was part of the founding management team at e-gaming company Betfair, joined the board of the peer-to-peer lender as a non-executive director in November 2011 – just 13 months after the company’s launch.

Data gatherers should be regulated like financial advisers (Financial Times), Rated: A

There was a time in the UK when most people were under the impression that financial advice was free. They went to see an adviser. He gave them advice. They handed over their money to him to be looked after. They never got a bill.

Only when the government changed the laws in 2012 did they realise they were paying. A lot. They just hadn’t noticed for the simple reason that they did not physically pay it to the adviser.

Deal maker Numis on the front foot after M&A numbers surge (Evening Standard), Rated: A

The City broker, which worked on the Trinity Mirror takeover of the Daily Express, and the sale of cocktail bar Revolution, said sales would be “significantly ahead” of £53 million recorded last year.

Since September, the company has worked on big deals like  the Mirror-Express takeover, the £600 million float  of car insurer Sabre and  the Aveva tie-up with Schneider’s electrical business.

Numis is also lined up to work on AJ Bell’s £500 million float and Funding Circle’s £1.5 billion float later in the year.

Shares rose 1.5%, gaining 5.5p to 365p.

One year to Brexit: How to protect you finances (Money Observer), Rated: A

Brexit is officially one year away and the impact it is having, and will continue to have, on our financial lives is filling the pages of our newspapers and TV screens daily.

Foreign currency

Since the Brexit vote in June 2016, sterling has fallen significantly in value against the euro. The pound reached a high of €1.42 in October 2015, but at the time of writing on 20 March 2018, it was worth 21 per cent less at €1.14, according to currency specialist Moneycorp.

Typical transaction costs for using your card abroad are between 2.75 and 2.99 per cent, and you will be charged a non-sterling purchase fee of up to 1.25 per cent on top.

Each time you use an ATM abroad, you can also be charged anything from £1.50 to £2 a time, so it is wise to withdraw larger sums in one go or to get a specialist overseas card that allows fee-free spending and cash withdrawals, according to Nick England, chief executive of travel money firm EasyFX.

Where are the current UK BTL hot spots? (Property Reporter), Rated: B

The latest UK buy-to-let index from property finance experts, LendInvest, has shown that the Midlands appears unaffected by the UK’s current house price growth slowdown, sending three of its largest cities into the top 5 places to invest – but where came out top?

  1. 1.  Colchester
  2. 2.  Northampton
  3. 3.  Leicester
  4. 4.  Luton
  5. 5.  Birmingham
  6. 6.  Manchester
  7. 7.  Ipswich
  8. 8.  Brighton
  9. 9.  Rochester
  10. 10.  Norwich

Together appoints new regional development director (Bridging & Commercial), Rated: B

Together has expanded its professional sector team with the appointment of Mel Fourie as its new regional development director.

Mel joins the specialist lender from RateSetter, where she was its strategic partnership manager covering the North of England.

China

Big four China banks report first drop in bad loans in 6 years (Asian Review), Rated: AAA

Industrial and Commercial Bank of ChinaChina Construction BankAgricultural Bank of China and Bank of China had a total of 765.7 billion yuan ($122 billion) in non-performing loans on their books at the end of 2017, marking a 0.2% drop on the year.

Non-performing loans ratios — a gauge of asset quality — averaged 1.57% at the four state-owned lenders, 0.15 percentage point less than at the end of 2016. “Special mention” loans with an elevated potential for default decreased 0.9% to 1.59 trillion yuan. All four banks had reported 2017 results as of Thursday.

Famous for hoarding cash, Chinese families are now racking up debt on an unprecedented scale (South China Morning Post), Rated: AAA

Chinese families with their long tradition of saving money are now accumulating debt at a rate never been seen before, according to data compiled by a state-backed think tank in Beijing.

The country’s household leverage ratio – or the ratio between debt incurred by families and gross domestic product – surged to 49 per cent at the end of last year from 17.9 per cent at the end of 2008, going up about 3.5 percentage points annually, the think tank said in a report released on Thursday.

So in the period from 1993, when the data became available, to 2008, the household debt ratio went from 8.3 per cent to 17.9 per cent, with an annual rise of 0.65 percentage points.

According to its report, average disposable income could cover standard loan interest and mortgage repayments, while households were still sitting on 70 trillion yuan (US$11.13 trillion) worth of bank deposits and cash overall – enough to offset the 40 trillion yuan in outstanding bank debt.

Dianrong and Sino Guarantee Announce Lenders Protection Plan (PR Newswire), Rated: A

Dianrong and China United SME Guarantee Corporation, known as Sino Guarantee, one of China’s leading guarantee companies, today announced a new lenders protection plan for Dianrong customers. The plan, which went into effect at the beginning of 2018, is designed to provide third-party protection in the event of a loan default.

Dianrong’s borrowers now have the option to purchase the Sino Guarantee lenders protection plan, which further improves the borrower’s risk and credit profile. Sino Guarantee will then use a dedicated fund account to pay lenders the loan principal and any outstanding interest in the event of a loan default covered by the plan.

European Union

Spotcap Roundtable: Customers Expect More as Fintech Boosts User Expectations (Crowdfund Insider), Rated: A

Spotcap, an SME focused online lender based in Berlin, recently held a roundtable on the future of finance. Individuals from prominent firms joined with the Fintech lender to assess progress made so far. Representatives from Deutsche Bank, Figo, GP Bullhound, McKinsey along with Spotcap debated how the financial ecosystem model will evolve, the implications for the customer, and the challenges and opportunities for Fintechs and more traditional financial service firms.

International

Alt.Estate To Become an Industry Standard for Blockchain-Based Real Estate Transactions (the Merkle), Rated: A

Using the blockchain technology to disrupt the real estate market, Alt.Estate has a strong potential to become an industry standard for the blockchain-based real estate transactions. A strong technology stack, a go-to-market strategy with 10X leverage, a working prototype with three tokenized apartments in key geographies, strong community support, and a solid pipeline of enterprise deals all position Alt.Estate as a win-win solution for real estate developers and investors.

Estimated at $217 trillion, the real estate market is worth nearly 2.7 times the global GDP.

“ERC-20” FOR REAL ESTATE

Developed two years ago, ERC-20 has quickly become a significant industry standard for all the tokens on Ethereum. Inspired by the approach of ERC-20 developers, Alt.Estate’s Protocol aims to become an industry standard for the tokenized real estate.

 

Asia

This Startup Combines FinTech And Traditional Lending Circles To Empower Women (Forbes), Rated: AAA

When Fonta Gilliam joined the foreign service out of school, she didn’t expect it would lead her to entrepreneurship. But after seeing community lending in practice throughout her work in East Asia and Africa, Gilliam wondered what would happen if she combined these traditional practices with new financial technology.

It all started when Gilliam was working in the visa department at the embassy in South Korea.

The practice of a lending circle is a kind of informal savings program. Say you have 10 members who each put $100 into the lending club each month. One member collects the full 1,000 each month and each month the total amount rotates until 10 months has passed and the circle starts back at the beginning.

Proptech – the emerging disruption in real estate (The Business Times), Rated: A

In proptech, three forms of technologies are particularly pertinent and pervasive: blockchain, augmented reality (AR) and artificial intelligence (AI). In Singapore, these technologies are already making their impact felt in the real estate industry with their adoption by startups, global corporations and the government.

Blockchain: facilitating real estate transactions

A form of distributed ledger, the blockchain is distributed across nodes, locations and even countries. Being decentralised, it eliminates the need for an intermediary to process, validate or authenticate transactions.

Artificial intelligence: extracting insights from data

The most valuable tech companies (Facebook, Amazon, Netflix, Google) are where they are today because of the trove of consumer data they possess and continue to accumulate. In the realm of technology, data is wealth, and AI is the key to unlocking this wealth. AI, as the name suggests, is teaching the computer to think like a human, making sense of the data fed to it.

Proptech: transforming real estate in Singapore

Cognisant of the need to keep up with change, the Singapore government has introduced an Industry Transformation Map (ITM) for the real estate industry. The ITM is focused on using automation, digitised contract templates, and predictive systems to streamline processes for property transactions and facilities management.

MENA

How GISC LoanCoin Network (GIS) is democratizing Credit with its Blockchain P2P, B2B & Altcoin Lending Platform (MenaFn), Rated: AAA

GISC uses a strict proprietary model composed of a fundamental and technical analysis strategy. So when their analysts suggested that cryptocurrency are poised to outperform in developing nations for some years to come, the company dedicated to coming up with a solution that would basically democratize lending of blockchain digital assets on a global scale.

GISC LoanCoin Network platform is designed with an aim of bridging traditional lending services to the blockchain and opening access to the non-banked. GISC LoanCoin Network is a Ethereum blockchain utility token based lending platform that will support P2P and B2B lending by eliminating intermediaries like banks and other financial institutes. It’s a platform where borrowers can interact and deal directly with the lender and GIS token holders can earn income by becoming Lenders or Guarantors.

GISC differs from other lending platforms like ETHLend and SALT in a way that GLN lends against Altcoins and this feature is yet to be introduced by any other lending platform. The platform usually takes a low, around 2% transaction fee for credit assessment/KYC-AML/ ID verification and connection through the network.

Authors:

George Popescu
Allen Taylor