12 Minutes Read.
Although alternative digital lending is still in its infancy, it has made a major mark in the consumer and SMB lending industry. A survey conducted by American Banks Association claims that:
the volume of loans originated by digital lending platforms will rise to USD 90 billion in 2020 in the US, constituting about 10% of the total lending market.
The reason for this rising popularity of digital lending is the hassle free and fast processing of loan applications compared to the slow and cumbersome process of raising a loan from a bank. Moreover, there is a lack of transparency and predictability in the traditional lending sector.
The fintech lending industry employs multiple digital technologies such as artificial intelligence, machine learning and blockchain technology to stay ahead in the tech arms race. These technologies are proving to be pivotal in determining how the lending industry will get reshaped in the next decade.
Leveraging AI, ML and Blockchain in Lending
Loan documentation used to be a bulky process but now lenders are employing artificial intelligence that makes the process not just faster but also error-proof and secure. The AI works hand-in-hand with machine learning algorithms to mark the credit worthiness of an individual. AI can process the applications within seconds, making the approval process truly scalable. This also reduces the chances of default and higher risk loans as it benchmarks the market data to categorize loans with higher chances of default.
Thus, AI makes the process of lending fast and efficient, cutting down both expenses as well as manpower. Even more importantly, it does not allow the personal bias of the credit officer cloud credit decisions.
Artificial intelligence also helps firms to fine-tune their systems and therefore, prevent them from hackers and cyber-attacks. Moreover, artificial intelligence algorithms have the ability to automatically identify the necessary data and segregate it from the bulk of information. AI is a critical enabler in not only in digital lending but in traditional commercial and consumer banking. It has replaced the branch-led model with a credit algorithm that is impartial and structures its decisions only on the basis of cold hard data.
Read more about AI in finance here: Artificial Intelligence in Finance – a Comprehensive Overview
Machine learning is usually clubbed with AI but has grown to warrant individual attention at the CXO level.
It not only empowers the automation of loan processing but is also the secret sauce on how credit algorithms become better with use of data.
Machine learning algorithms gather data at a large scale, consolidate this data and identify patterns in this data from its own lending history. Based on these patterns it judges whether a borrower is credit worthy or not and regulate the process of loan decisioning engine. So AI may help you make credit decisions at scale but it is Machine Learning that helps you improve your algorithms and ensure that you are one step ahead of industry in understanding credit and market patterns. Its application ranges from credit models, identifying frauds to target marketing and rate setting.
Build on distributed ledger, blockchain makes the process of lending decentralized and trustless. The aim of introducing P2P lending on blockchain is manifold.
- It can help establish a direct relationship between the lender and the borrower by removing the intermediary or middleman from the process. On a blockchain platform, a lender can easily transfer funds to the borrower by transferring the ownership of his assets by means of smart contracts. The entire loan transaction can be run on blockchain and executed though smart contracts without any central agency.
- It can tokenize/securitize the loans into micro-assets that can be made tradable by breaking them into units like shares. Though this is already happening at an institutional level, blockchain can scale this to attract investments by even small retail investors. A functioning market for the marketplace loan will generate liquidity and better price discoverability.
- Lending US dollars against crypto assets is turning into a billion dollar market as the value of cryptocurrencies has grown to almost $300 billion.
Banks and Digital Lending
With the traditional lending procedures becoming more and more complicated every passing day, customers are getting frustrated with the process. The stringent regulatory norms, the complicated KYC process and the amount of time required to process the application makes it almost impossible to raise loans on time. On the other hand, digital lending platforms provide an easy, fast and transparent process of raising loans. The borrowers can apply for a loan sitting at home, no need to move back and forth from bank to home. The entire process is fully automated and documents can be directly uploaded along with the form which saves them from carrying huge stacks of paper to the bank. The application gets approved within a couple of days and the loan gets passed in no time.
All this is pulling customers away from banks, towards non-banking financial institutes which provide customers with specialized and agile solutions that focus on the demands of the customers, keeping them satisfied.
With non-bank lending gaining momentum, banks are looking at ways to incorporate these rising technologies into their protocols. They are engaging with many digital lending platform to make lending a satisfying experience for their customers. However, a recent estimation by Bain and SAP Value Management Center claims that
…only 7 percent of banking processes are handled digitally..
even though digital lending saw a growth of 93 percent in 2015 and 58 percent in 2016.
If banks keep on relying on traditional methods, they will not be able to meet the rising expectations and demands of their customers and will eventually lose them. This will hamper their growth and productivity. It is high time that banks look forward to restructure their loan processes to be able to match the standards of non-bank lenders.
Cost Down. Speed Up.
Adopting digital technology and cutting down the cost of manual labor and heavy documentation, banks can make the loans more cost-effectively . It will also make the process fast by cutting down the time required to verify the customer information. Also, the AI and ML algorithms help make the management of data easy. All of listed significantly enhances the efficiency of banks’ lending process.
According to a study by KPMG, robotic process automation can cut costs in financial services by 75%. Almost every single fintech lender is leveraging AI and ML for lending. Players like Upstart, Amazon Lending and Avant are investing millions of dollars to integrate AI and ML for their loan application process. The integration starts right from the very first click on the website by the borrower to disbursement and final repayment of the loan. The data is being used for analytics and to create patterns that give lenders an edge in lending and pricing loans. AI and ML are replacing the age old FICO score by accounting for factors like the applicant’s school, online profile and even the time of loan application.
Find out what Artificial intelligence solutions for credit scoring exist today: AI for Credit Scoring – An Overview of Startups and Innovation
Humans cannot analyze such a diverse set of information. AI and ML help sort these parameters into meaningful, actionable insights.
Blockchain’s use case is extremely limited but pilot projects are going on in JP Morgan and Goldman Sachs along with a host of lending startups to drive value and scale from decentralization and smart contracts. US based Spring Network, Gem and CULedger project are all targeting blockchain as a solution for exchanging critical yet anonymized personal borrower data. This would eliminate the chance of an Equifax type hack where millions of borrowers had their personal data stolen. With data being stored in billions of blockchain nodes, it is nearly impossible to hack the complete blockchain altogether.
Impact and the Benefit of getting on the bandwagon:
The impact on banks and credit unions is for all to see. Nimble fintechs with a fraction of physical reach, established customer base and operational budget of established traditional lenders have leapfrogged the lending tables especially in the personal and SMB lending space. Their unit economics work because of AI and ML as these technologies allow lenders to take credit decisions centrally and process applications without the need of a physical branch.
Brick and Mortar Lenders need to co-opt these technologies for stronger profitability, rollout of online only products and reduce operational costs related with sanctioning a loan.
Waiting now can lead to a permanent moat in favor of fintechs and banks that have invested in these technologies to cater to millennials. Blockbuster had a chance to buy Netflix, but now is relegated to history books. Banks are facing their own existential crisis.
The banks can look to invest in their current IT systems and go for an in-house overhaul of their application and credit evaluation processes. But unless you are really to shell out billions of dollars on tech, it does not make sense in rebuilding what is already there in the market. MonJa with its digital lending solutions in loan scoring, risk management and portfolio management can help you scale your tech platform as per the demand.
AI, ML and Blockchain are revolutionary technologies. They are disrupting industries in all spheres. Banking and lending is no different. AI and ML are now mainstream technologies for lending startups but they have a long way to go before they reach any kind of saturation. Blockchain projects are being launched in many banks and startups and it is only a matter of time before we see major applications for blockchain emerging in the lending universe.
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