February 2020 - Get Insight - Insight Consultants

Due to its surge in popularity, and fast transaction cycles, online lending is a prime target for cybercriminals. Though financial institutions may have always had customer security in mind, the industry has felt the backlash in recent years. 70% of lenders regularly put sensitive financial data at risk by prioritizing customer convenience over security. With the growth in technology, cybersecurity threats are also on the rise. Hackers are employing more sophisticated techniques to commit crimes in the cyberworld. As a result of this growing threat, there is a significant need for firms to recognize cyber threats and craft preventive measures.

Digital lenders are more vulnerable

Today’s lending industry sits in the eye of a perfect storm, driven by three key components:

Traditional lenders are all going digital. Enabling disruptive business models such as mobile banking catching the attention of hackers.
Massive data breaches are throwing enormous amounts of personal financial data out on the dark web
Instant-decision software systems, often supported by third-party vendors, create a variety of vulnerabilities that cybercriminals are ready to exploit

Online lenders are under increasing pressure to adopt smarter authentication methods that leverage real-time, behavior-based intelligence to accelerate genuine loans and prevent fraud.

Role of AI in cybersecurity

In this scenario of increased cyberattack, AI mechanisms are emerging as the means to strengthen cybersecurity and thwart attacks. Here’s how AI comes to the rescue of online lenders.

1.Real-time fraud identification: AI helps data scientists efficiently determine which transactions are most likely to be fraudulent, while significantly reducing false positives. The techniques are extremely effective in fraud prevention and detection, as they allow for the automated discovery of patterns across large volumes of streaming transactions.

2.Predict and counter new-age fraud: Enable organizations to stay one step ahead of fraudsters. Predict and tag any suspicious behavior that might lead to a new type of fraud. Here are some guidelines for best practices:

Build a solid foundation
Detect fraudulent loan applications
Prevent account takeovers
Identify cross-device use
Deploy a cloud-based lending platform

3.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.

4.Risk factor detection: AI can match business owners with the right lender. AI analyses and authenticates users’ transactional data, and income verification and spend analysis helps highlight risk factors used for a richer credit scoring experience. This will reduce the risk of default and increase borrowers’ financial profiling.

5.Authentication Process: AI quickly detects any suspicious behavior. It can analyze hundreds of thousands of visitors and categorize them based on their behavior and threat level in seconds. It can improve the authentication process to a much accurate degree.

**Is AI the answer to all cybersecurity issues?**

AI is equipped to deal with large volumes of data from numerous sources and capable of spotting abnormal patterns and links that humans are not able to identify. Fraud detection methods today are evolving from being rules-based towards pattern recognition. Here ML can easily recognize patterns and un-usual consumer behaviors. It also protects companies from insider fraud as it can study data access from within the organization and identify any anomalies in individuals deviating from their day-to-day jobs or exposing data to outsiders.

While AI is doing wonders for cybersecurity, it is also making its way into the employ of hackers for malicious purposes. In the wrong hands, it can do exponential damage and become an even stronger threat to cybersecurity.

At Insight Consultants, we help our clients providing actionable advice to begin an AI initiative with the right approach and perspective. We can help you in developing Intelligent self-learning software that can see patterns and help lending business eliminate security threats. If you are you looking for ways to harness the power of machine learning and AI to protect your business against cyber threats, or would just like to know more, contact us.

Photo by cottonbro studio: https://www.pexels.com/photo/hand-holding-a-usb-flash-drive-5474288/

Loan approval has become much easier in the recent past. But debt collection is still a daunting task which affects the productivity of lenders and slow down their operations. With increasing consumer debt, the traditional debt collection methods are no longer viable with modern customer expectation. As a lender, preparing for debt collection issues and effectively responding to delinquent customer-debtors is a must to ensure continued business growth and success.

Major challenges in debt collection:

Lack of customer data
Increased burden of regulations
Failure to track and reconcile accounts
Inability to execute new recovery strategies

Traditional debt collection is driven by focus on delinquent accounts and handled by a part of the business detached from customers. Lenders need to shift their own methods to match customer preferences- which are clearly for digital channels. Advanced analytics and machine learning can help financial organizations to have a deeper understanding of their at-risk customers. AI can help in segmenting better by considering a span of other parameters like customer behaviour along with their credit history.

Alleviating risk and elevating customer experience

Essentially firms require a technology like AI that can sit on top of their existing collection systems, act on the vast amount of data accumulated over the years, consider other parameters like behavioural and sentiment analysis to derive an effective collection strategy.With AI based segmentation approach, firms can enhance customer experience with personalized collection and communication strategies. ML algorithms can devour high volumes of unstructured data and provide actionable insights. The built-in AI helps these applications to identify the right segment accounts that are not only based on financial ability of the customers, but also their behavioural patterns from calls, text messages and social media platforms. Thus, it helps to predict customer delinquency, assist recovery agents to call the right customer, set the right tone and gain an upper hand in negotiations.

Quick steps to make debt collection win-win

Customer segmentation
Auto generation of customer statements
Omni channel orchestration
Customer activity monitoring
Digitized collection strategy

With the impact of digital transformation in the finance sector, it became increasingly evident that AI and ML will define the future of consumer lending. Additionally, customers of this digital epoch seek easy and convenient solutions, which further compels consumer lending companies to adopt an ML-based, data-driven loss mitigation model that is capable of analysing large volumes of structured and unstructured data. Leveraging the power of AI, organizations can drive a customer risk segmentation achieved from data driven intelligence applications that find the right balance between mitigating loan losses and enhancing customer experience and make meaning out of it. Online banking and virtual collections agents could increase payments and reduce costs for call centers, while improving customer satisfaction.

This is a win-win for the entire financial ecosystem with happier borrowers and wider business opportunities for lenders

With advances in Artificial Intelligence and Machine Learning, it is possible today to make loan default management hassle-free. Based on access to large volumes of unstructured data, AI can predict red flags or green lights when it comes to onboarding consumers in the first place, and where credit is first extended. This can help identify potential instances of default and initiate action before it occurs.

AI, the next wave of opportunity in debt collection

Historically, lenders used to make go-no go loan decisions based on a loan applicant’s credit score. Digital lending platforms believe that this kind of information does not paint a complete picture of a loan applicant’s creditworthiness. Firms can use artificial intelligence which is based on the premise that machines can learn and adapt from experience, rather than rely exclusively on pre-programmed logic.

Online brokers, lenders and credit bureaus use algorithms to assess eligibility for credit. On the flip side, AI can also match business owners with the right lender. AI analyses and authenticates users’ transactional data, income verification and spend analysis helps highlight risk factors used for a richer credit scoring experience. This reduces the risk of default and increase borrowers’ financial profiling.

Leveraging Artificial intelligence for debt collection

Implementing AI in loan default management helps lenders reimagine the delinquent customer journey. Collections is not just about recovering overdue instalments and regularize their loan accounts. It goes beyond suggesting a way out of the crisis and that is when AI can act as a bridge between lenders and customers.

Identify potential defaulters: Chatbots built using machine learning (ML) and natural language processing (NLP) technology helps to analyse customers’ digital interactions to identify customers facing adverse financial situations.

Reduce Unproductive Agent Time: Virtual Assistants (AI Assistants) answers inbound phone calls that can provide a great opportunity to automate self-service for non-revenue generating transactions. This reduces the influx of unproductive inbound calls received by the agents.

Augmented Intelligence: With the help of augmented intelligence, collectors can use real time analysis to guide the conversation with the debtor in the right direction.

Real-Time Analytics: Using advanced analytics and applying machine-learning algorithms, lenders can move to a deeper, more nuanced understanding of default customers. With this more complex picture, customers can be classified into microsegments and more targeted—and effective—interventions can be designed for them.

Automated Transactions: With AI, firms can reduce the overall investment by automated processes and significantly condense the price of serving customers.

AI-powered solutions can potentially transform the way collections are handled and ultimately help lending firms to improve customer experience and create exponential value.

AI implementation made simple with Insight Consultants

While the potential of AI in loan default management is immense, it requires substantial investment in infrastructure to enable collection and storage of data, as well as skilled resources. We can help you provide

Actionable advice to begin an AI initiative with the right approach and perspective
Implement AI for accurate risk segmentation, delinquency prediction models
Build AI-powered analytics framework
Supplement existing processes with AI for higher operational efficiencies

So, if you are looking for ways to harness the power of machine learning and AI in your debt collection strategy, CONTACT US

Research by Thomson Reuters suggests that technological advances such as cloud computing and real-time data will have the biggest impact on the corporate finance and accounting function in the next ten years!

Fast growing Start-ups and mid-cap companies will be very familiar with this problem – at the initial stage, there is a lot of work done with spreadsheets. The main areas of interest are:

#Proformas – The business model is usually forecasted for a year on how it should perform by design. The idea is then to track the actual performance of each function in the business model against this design document and formulate early actions for corrections as necessary.

#Budgets – The more astute ones would budget on a quarterly basis to have a tight control on financial management. Sensitivity analysis on budgets are always yearned for when making decisions on staffing, IT spend etc.

#Accounting cycle closure – A monthly closure of the books after recording everything accurately is very important to ensure that financial data used is credible. While cloud-based accounting, applications provide the backbone for this, various reconciliations call for spreadsheets in active use.

#Management reports – Usually generated on demand and often someone in accounting is tasked with creating these on spreadsheets.

Very soon, the business leaders will realize that while spreadsheets are very versatile and often unavoidable, even within a robust technology infrastructure, getting insights through real time dashboards to drive the business in a competitive environment requires more than what they offer.

(A few illustrations of the dashboard visualization and data sources used are also provided if you are interested in a closer scrutiny. The specimens pertain to a business in is the alternative lending industry.)

3 Steps to a real time Dashboards

Based on our experience of enabling a fast-growing startup from inception to a mature phase, involving real-time dashboards, here are 3 steps to make the transition, for any business in a similar situation.

From management reports to dashboard visualization.

The corporate finance and accounting function need to work closely with the executive leadership to generate reports on demand. If you are struggling for resources or the right mix of talent, you must think of virtual teams that can engage remotely to boost your current capability. Across a few cycles of report generation and reviews, you will arrive at sufficient consolidation required for visualizing a dashboard. (Illustration: Consolidated graphics for the lending business that form a real-time dashboard)

Mapping information flows from the data sources.

A fast-growing business means a dynamic environment even for the technology infrastructure, with applications making their way in and out. Having a clear mapping of data sources and information flows towards useful reports is very important at this stage. (Illustration: Summarized data table)

Implementation of a BI tool.

With the first two steps in place, someone with skills on any BI tool, like PowerBI can build the necessary integrations into the data sources and apply the necessary transformations to realize the dashboard that has been already visualized.

If you are one of those businesses that find yourself in the place where you are ready to take the journey from spreadsheets to dynamic dashboards, by optimizing technology you might already have or can easily procure, feel free to Get in touch with us that will review more specifics that will help you get started.

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