In today’s digital era, attracting, acquiring, and onboarding new customers through digital channels
Category: Banking Financial Services
With the rise of online platforms, the average thief has shifted their operations from the streets and train stations to the digital realm. Online lending, in particular, has become a lucrative target for cyber criminals due to its popularity and rapid transaction cycles. While financial institutions have always prioritized customer security, the industry has experienced the repercussions of this evolving threat landscape in recent years. To combat the ever-present risk of fraud, the integration of AI in fraud detection has emerged as a vital defense mechanism for the industry.
Yet, as the saying goes, knowledge is power. You can make the best utilization of the convenience provided by technology aiming yourself with the power of knowledge. Cyber-attack methods and tools keep evolving with advancements in technology, increasing the possibility of ingenious scams that can be deployed from anywhere across the world.
Role of AI in Fraud Detection
In this scenario of increased cyberattack, AI mechanisms are emerging as the means to strengthen cybersecurity and thwart attacks. Research reveals that 63% of financial institutions believe that AI can prevent fraud, while 80% agree that AI plays a critical role in reducing fraudulent payments and attempts to commit fraud. Machine learning technology can be deployed across multiple channels (e.g. transactions, loan applications, etc.) in the financial industry. Banks and financial institutions can benefit from patterns that emerge with use of AI and ML to prevent frauds even before they happen.
Leading ways lenders are using AI in fraud detection
Building purchase profiles: To accurately detect fraud, financial institutions must first understand what typical customer behavior looks like. Using machine learning to sort through vast amounts of data from past financial and non-financial transactions, FIs can build and slot customers into several different profiles.
Develop fraud scores: All transactions can be assigned a fraud score by using data from past legitimate transactions, incidences of fraud and risk parameters set by the financial institution. The score, which considers variables such as transaction amount, time, card use frequency, IP address of a purchase, and much more, is used to assesses the fraud risk involved with that particular transaction.
Enhance underwriting: AI can have far-reaching benefits for underwriting performance. Increasingly accurate loss predictions enable underwriting teams to spot good and bad risks, grow a profitable portfolio, and automate processes to streamline their workflow.
Fraud investigation: Machine learning algorithms can analyze hundreds of thousands of transactions per second. Investigating and prosecuting fraud claims can be incredibly time-consuming, so ensuring agents are armed with the proper tools to increase efficiency is essential.
Know Your Customer (KYC): AI-backed KYC measures can verify ID and documentation, match fingerprints and even perform facial recognition almost instantaneously. This powerful tool strikes the right balance between customer security and convenience.
Digital organizations can identify automated and more complex fraud attempts faster and more accurately by combining supervised and unsupervised machine learning as part of a larger Artificial Intelligence (AI) fraud detection strategy. There is no question AI is making cybersecurity systems smarter. Whether this technology is used for securing authentication, threat detection or bot battling, AI and ML can prevent bad actors from infiltrating and manipulating company networks.
Insight Consultants’ fraud detection strategy using ML
To detect fraud,
- The machine learning model collects data
- The model analyzes all the data gathered and segments and extracts the required features from it.
- Predict the probability of fraud with high accuracy
An outdated financial system is always full of loopholes tricksters can use. Luckily, machine learning can potentially improve bank fraud detection with data analytics and help nearly every industry.
If you are looking for ways to harness the power of machine learning and AI for your business or would like to know more, Contact Us.
Loan application fraud is a lender’s nightmare. It’s a real issue faced by many financial firms. It is estimated that financial institutions lose billions of dollars yearly to this type of scheme, with synthetic identity fraud alone being responsible for over six billion dollars of credit losses. Without face-to-face interactions, fraudsters and thieves attempt to use stolen identities and fictional financial data to commit online financial crimes — believing it to be an easier or more successful prospect.
Application and identity fraud prevention
When fraud happens, it comes with a cost to lenders. In the US alone, close to 300,000 people fall victim to credit fraud every year. In this situation, fraud mitigation must be an integral part of any lending risk management plan.
To achieve a successful fraud prevention strategy, FIs must conduct a balancing act between security and customer experience. They need to put in place an account opening process that includes real-time risk assessment and identity verification while delivering a digitally seamless customer experience. The importance of improving the customer experience cannot be understated. It is best for businesses to guarantee strong identity verification and fraud prevention, as every little mistake in this process can put the customer at risk and damage the reputation of the business. Ensuring a secure account sign-in process builds trust with users and attracts and retains more customers.
FIs can battle fraud on loan applications in a wide variety of ways:
- In-depth monitoring of new account application data
- Monitoring of existing accounts for suspicious activity patterns
- Identity verification to prevent loan fraud
The most common, widely used loan fraud detection method is identity verification testing, and let’s see how identity verification helps in reducing loan fraud.
Digital identity verification to lessen fraud attempts
Fraudsters exploit vulnerabilities in detection by compiling fake applications, or synthetic identities, that are a composite of several different identities. To prevent application fraud, financial institutions must successfully identify fraudulent activity or fraudulent identity documents in real-time at the beginning of the new account opening process. AI-powered ID verification is a great way to authenticate users at scale without sacrificing security while still gaining an edge on scammers.
Digital verification procedures that are part of a well-developed CIP and Know Your Customer (KYC) practices reduce the chance of synthetic identity fraud, while virtually eliminating doctored documents. Through consumer-permissioned (transactional and account-level information that a consumer gives a business permission to access on their behalf) access to financial data, verifications can be based on or validated by information direct from a financial institution. This is dramatically better than relying on documents that have changed hands at least twice in the loan application process.
AI- powered digital verification by Insight Consultants
Insight Consultants AI-Powered Document Verification Solution is was designed to give financial institutions an easy-to-use, compliant, secure, and cost-effective method of loan application fraud prevention. AI-powered identity verification provides optimal fraud prevention and ensures highly effective authentication in compliance with KYC requirements.
Using consumer-permissioning provides an additional layer of protection as it requires the applicant to know unique personal identifying information (PII) for each financial account they intend to use. As they go through the digital verification process, several aspects of an applicant’s identity are challenged. A fraudster would require access to PII to successfully launch a digital verification, which is nearly impossible.
In case you want to take your business security to next level, write to us at connect@insightconsultants.co. Else connect with us swiftly, fill out our request form here
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The coronavirus crisis has escalated the need for financial institutions to digitise their processes. The digitalisation of a financial institution’s lending process is no longer an option, but a requirement in this current economic climate. Firms must consider the compelling benefits of artificial intelligence (AI) when digitalising their credit process to overcome the COVID-19 economic crisis and stay ahead of competition.
Covid-19 impact and how to emerge stronger with digitalization
Lending is one of the areas that has significantly been affected by the pandemic. In response to the pandemic, businesses must focus on digitalization.
Using new data and AI to improve business: Companies need to incorporate new data and create new models to enable real-time decision- making. Accelerate process automation
Refocus digital efforts towards customer expectation: Align the organization to new digital priorities. Launch new digital offering channels.
Selectively modernize technology capabilities: Begin strengthening technology talent bench. Set up a cloud-based data platform and automate the software delivery pipeline.
Upskill organization for accelerated digital efforts: Deploy new models leveraging agile and remote.
Lend more and smarter using ML & AI
Digital transformation was never just about technology. With digitization, businesses can drastically lower the operational costs, increase efficiency and speed of decisions. Automating the processes can reduce risks by employing advanced scoring techniques to supplement the traditional approaches and data sources.
Using AI in lending shows up in several productivity-enhancing aspects, including
Forecast cash-flow: Cash flow is likely to continue to be a serious concern for smaller businesses as revenue streams dry up. Multiple AI and Machine Learning algorithms can process datasets including inflows/outflows, sales orders/customers invoices, purchase orders/vendor invoices and expense reimbursements for comprehensive as well as accurate cash flow forecasts.
Predict future losses: COVID-19 has brought about a stressed financial environment that affected credit quality and credit losses. An AI dashboard uses various criteria points that can help in predicting and preparing for these losses by highlighting patterns and trends—right down to the loan type, region, branch location, etc.
Sales prioritization: With AI, the algorithm can compile historical information about a client, along with social media postings and the salesperson’s customer interaction history (e.g., emails sent, voicemails left, text messages sent, etc.) and rank the opportunities or leads in the pipeline according to their chances of closing successfully.
Agile risk management: AI can complement the internal controls and early warning systems already in place around loan approvals, disbursement, and monitoring. A strong AI dashboard can also provide regular insights on the overall health and status of your loan portfolio in real-time, allowing you to make more accurate risk assessments and pivot as necessary.
Enhanced decision-making: In a post-COVID-19 world, lenders will likely exercise greater caution when it comes to credit risk. AI can help flag potential problems and potential biases at the loan authorization stage. It can provide managers with greater visibility and access to data, to make decisions that align with the organization’s risk appetite and policies.
Back office tasks: AI-powered cognitive assistants can perform a company’s back-office tasks effectively
AI- an evolving technology
Artificial Intelligence is the future. Sure enough, the technology is young and has its drawbacks. It requires high costs, and its implementation is both time and effort consuming. But unrealistic expectations that used to generate fuss around these technologies have turned into real business scenarios. It is especially noticeable in the field of finance. Embracing Digital Transformation driven by digital technologies can help lenders grow their loan book and acquire more customers.
Specifically, under the larger umbrella of digital technologies, Artificial Intelligence (AI) is the differentiator. AI can unearth and learn customer-behaviour patterns that help lenders differentiate themselves from the competition. Let’s look at a couple of high-impact areas that AI can influence significantly in terms of transformation, and help lenders improve their loan books.
If you, are you looking for ways to harness the power of machine learning and AI for your business, or would just like to know more, Contact Us.
During the COVID-19 crisis, buy-now-pay-later (BNPL) emerged as a hot segment within consumer lending when a large portion of consumer spending moved online. By venturing into BNPL space, financial firms can create a niche by transforming into two-sided marketplaces where they not only facilitate transactions but also provide a marketplace for discovery of new products. This enables access to granular customer data that can be used for credit assessment.
How lenders can enter the BNPL space
Participating in the BNPL space will require financial institutions to invest significantly in technology capabilities as well as marketing initiatives. Lenders who want to take advantage of the opportunity in BNPL can try few different approaches and increase their chances of success in the BNPL space.
Customer Affordability as a Service: As economic activity rebounds, people are facing elevated levels of inflation with potential increases in interest rates forecast in early 2022. To meet the increasing regulatory requirements and to ensure good customer outcomes, firms need to work towards more customer-centric affordability assessments. The introduction of consistent modelling for income and outgoings across all areas of the business will give lenders increased control, consistency, and agility to react to regulatory changes.
Purpose driven: Lenders can determine what each customer can afford, educate them, and help them avoid overspending by managing their overall limit and exposure.
Holistic approach : Firms can manage the merchant acquirer and card issuer businesses holistically, which will enable them to run connected campaigns and manage a combined P&L for the two businesses.
Loyalty and personalization: Lenders can proactively create offers and manage real-time personalization of both in-store and e-commerce purchases. When there are multiple offers at the checkout, customers will choose the most personalized and relevant option. Banks will be forced to be contextual and relevant at the moment of purchase.
Charging into to the BNPL battle
Traditional card issuers and consumer lenders can’t afford to ignore the BNPL trend-some have already launched and others are working on their own variation. In its simplest form, BNPL is not a new proposition, and usually involves offering customers the facility to break payments for goods and services into multiple installments.
Lenders can increase customer engagement, wallet share, and loyalty by offering seamless, convenient shopping experiences. BNPL can benefit incumbent banks that can use existing strengths, such as better loan term and condition flexibility and increased capital utilization owing to quicker loan turnover and lower regulatory capital requirements. BNPL also provides cross-selling opportunities to bank and non-bank customers who are likely to be more engaged.
As far as customers are concerned, even though they pay in installments, they gain full ownership of the product. The fixed payments help them budget their expenses. Customers also tend to prefer BNPL for purchases under a threshold such as $500, and anything over that, they prefer instalment plans that can stretch up to a year.
Key takeaways
- Buy now, pay later arrangements are point-of-sale installment loans that allow consumers to make purchases and pay for them at a future date.
- Consumers typically make an upfront payment toward the purchase, then pay the remainder off in a predetermined number of installments.
- Buy now, pay later plans often don’t charge interest and are often easier to get approved for than traditional credit cards or lines of credit are.
- Normally, BNPL doesn’t affect your credit score; however, late payments or failing to pay can damage your credit score.
Road ahead
BNPL has suddenly emerged as a disruptive force in the payments and consumer finance industry. It needs to be seen as not just a new twist to merchant payments, but as a paradigm shift in how banks can engage with their customers and increase revenue. Existing and emerging digital payments require embracing technology and innovation along with a safer digital experience to win the trust of customers.
In the tech-driven payments space, open banking and real-time payments infrastructure is expected to create innovative payment solutions for customers, and new pathways to accept payments for merchants and corporates. To retain customer base and a competitive edge, Lenders must quickly foray into this segment. Accomplishing this, however, may require them to partner with a service provider with the necessary contextual knowledge and technology expertise after a comprehensive market analysis.
Credit Risk Management: Optimize Lending in 3 steps.
Given the enduring prominence of credit vulnerability as the primary risk factor within the global financial industry,
How Alternative data can help lenders in reducing financial risk?
Over the years, an individual’s or even an organization’s creditworthiness is defined by their credit score.
The pandemic has halted the proliferation of traditional paper-driven, face-to-face loans and forced commercial lenders to move toward digitalization. Additionally, fintech companies addressing the same issues are on the rise and are giving traditional consumer lenders a run for their money. Consumers are now moving towards creating bank accounts online to conduct transactions digitally. Meanwhile, an appetite for “Open Banking” is picking up pace worldwide.
Why Should Business Lending Use Open Banking?
Lenders require account data to make decisions on which businesses are eligible for loans. Open Banking is driven by regulatory, technology, and competitive dynamics. It calls for banks to use APIs to make certain customer data available to non-bank third parties. The innovation is evolving the industry toward hyper-relevant, platform-based distribution and giving banks a rich opportunity to expand their ecosystems and extend their reach.
Cloud-based APIs are at the heart of these digital transformation strategies. Simple plug-and-play functionality makes it possible for financial institutions to adopt an integrated environment of applications, all designed to automate critical workflows to return faster credit decisions. Aggregating data across multiple accounts into one easy-to-use platform, offers customers a 360-degree view of their spending and simplifies the ever-growing number of touchpoints they encounter daily. Open banking will also enhance real-time payments, going head-to-head with the card scheme to enable instant transactions between retailers and consumers.
Open banking allows for automated bank statement collection. It also provides data on the debt and cash flow profile of a business that enables business lenders to understand the financial health of a business.
The Future
From offering personalized insights to simplifying payment transactions, Open Banking provides the spark banks need to develop modern financial tools that provide even more value to their customers. It’s like a collaboration between traditional bank players and new financial players. Moreover, open banking is helpful for SMEs as well. Via APIs, fintech companies will be able to access different types of accounts, insurance, card accounts, and leases, and consolidate data from multiple countries into one frame.