Newsletter | January 2020 | Issue 101
Focus On
Making debt collection a win-win deal
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
Keep Reading
Using Artificial Intelligence for improved debt collection
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 AI in 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
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In the News
Strong consumer credit activity expected in 2020, says TransUnion
2020 will be a good year for consumer credits, says TransUnion. Their 2020 credit forecast shows credit balances and loan originations will grow next year. Major findings include, serious delinquency rates will either drop or remain stable for auto loans, credit cards, mortgages and unsecured personal loans. Total balances for all major credit products are also expected to rise for unsecured personal loans, credit cards, mortgages and auto loans.
Factors influencing the positive trends for credit activity include low unemployment rates, continued growth in the GDP and strong consumer confidence. The U.S. consumer credit market has now grown every year since the Great Recession concluded in 2009, marking one of the longest economic expansions in U.S. history.
TransUnion’s forecasts are based on various economic assumptions, such as gross domestic product, home prices, personal disposable income and unemployment rates.
Australia cracks top 10 place in global fintech ranking
As per the Global Fintech Index City Rankings 2020, Australia ranked in 8th position. The top country for fintechs was said to be the US, followed by the UK and Singapore. The success of leading Australian fintechs such as Judo Bank, MoneyMe, Airwallex and Afterpay has landed the country in the top 10 nations for fintechs according to a new global ranking of the ecosystem.
Australia’s largest city, Sydney, placed at 13th overall globally in 2019, while Melbourne, the country’s second-ranked fintech hub, came in a 32nd. Sydney and Melbourne were Australia’s only two cities in the world’s top 100 cities for fintech.The world’s leading city for the year was New York, followed by London and Hong Kong.
Sydney, the country’s top stop for fintechs, came in at fifth place for the Asia-Pacific region, behind Singapore as the number one city, Bangalore and Mumbai in India, and Hong Kong.
Simon Hardie, CEO of Findexable, which produced the fintech rankings report for the first time, said their findings revealed financial wealth is no guarantee of a city’s status as a fintech hub.
AI promises to aid financial firms in productivity gains
Artificial intelligence brings lot of promises to the financial services industry, whether it’s through automating processes or adding more convenience for their customers. A new report by the consulting firm, Accenture says that artificial intelligence could help to unlock more than $140bn by 2025. Accenture recently studied the changing face of the workforce as disruptive technologies become more prevalent in companies around the world. The consulting firm found 48% of tasks in the financial services industry could be augmented with technology by 2025, which will result in a big increase in productivity.
AI, for example, could aid financial advisors in making real-time stock picks or help loan underwriters better gauge the risk of borrowers. It could enable banks to offer customized products based on an individual’s personal finance habits. Banks are expected to generate $59 billion in productivity gains by augmenting skills with technology while insurance companies can expect to generate $37 billion in gains and capital markets companies are forecast to realize $21 billion in productivity increases
Credit Unions set to finalize mergers in 2020
While members are set to vote on 12 proposed credit union mergers in January and February, four credit unions announced that their members have given the green light for their consolidations to finalize in 2020. According to the NCUA, members of 12 credit unions across the nation are scheduled to vote on merger proposals next year.
Credit Unions agreed for merger include:
- The $54.9 million, 3,769-member Southwest Colorado Federal Credit Union in Durango, Colo. will merge into the $1.5 billion Credit Union of Colorado Federal Credit Union in Denver.
- The $31.7 million, 8,681-member Health Facilities Credit Union in Florence, S.C. will consolidate with the $1.8 billion South Carolina Federal Credit Union in North Charleston,
- The $24 million, 3,247-member Turbine Federal Credit Union in Greenville, S.C. will merge with the $1 billion Self-Help Credit Union in Durham, N.C.
- With both credit unions based in Alexandria, Va., the $28.5 million, 3,957-member AB&W Employees Credit Union will merge on March 1 with the $362 million Commonwealth One Federal Credit Union.
Events
FUTURE OF FINANCE AND CFO SUMMIT ASIA 2020
12-13 February 2020 at Singapore
Key Stats
Central Bank Interest Rates and Current Libor Rates
| GBP Libor (overnight) | Interest
(01-10-2020) | Central Banks | Interest Rates |
| Euro Libor | -0.57100 % | American Interest rate (FED) | 1.75% |
| USD Libor | 1.53313 % | Australian Interest rate (RBA) | 0.75% |
| CHF Libor | -0.78200 % | British Interest Rate (BoE) | 0.75% |
| JPY Libor | -0.11617 % | Canadian Interest Rate (BOC) | 1.75% |
| GBP Libor | 0.68225 % | Japanese Interest Rate (BoJ) | -0.10% |
