13 minute read
How artificial intelligence and automation is solving Credit Management headaches – B2B Identity Fraud being one of them
How artificial intelligence and automation is solving Credit Management headaches
- B2B Identity Fraud being one of them
By Miriana Lowrie*
Miriana Lowrie
The pace of technology development has a profound impact on most industries and our everyday lives. The world of trade credit management is no exception. From fraud assessment checks to business process automation, building scale through the cloud, to leading the charge on data-led decision making, the power of digital technologies should not be underestimated as your best-practice enabler.
Let’s helicopter up to the macro view first ...
You may be aware of discussion points around Web 3.0. Personally, I find the simplest way of describing what Web 3.0 is (otherwise referred to as Web3), is through the evolution of the world wide web (www): z Web 1: Read only (~1980’s-2004) z Web 2: Read and Write (~20042020), think: content creation, social networking, blogs, etc. z Web 3: Read/Write/Ownership (~2020-now)
Ownership. With Ownership we are talking about a fairer internet where everyone owns and can control who profits from their information. Here we will see blockchain becoming more prolific because it is the key enabler for individuals to own and be compensated for their data, through “Non-Fungible Tokens” (NFTs).
We will hear a lot more about NFTs, which are cryptographic assets on blockchain with unique identification codes and metadata that distinguish them from one another. NFTs are used to represent not only real-world items like event tickets, artwork, or real-estate but also to represent people’s
identities. This is where the authenticity or proof of ownership comes in.
For now, we all have access to world class technology such as AI and identity technology, very easily accessible and cost-effective ways of helping us reduce risk. While your business may not be an early adopter, being a fast follower will ensure that you derive benefits quickly and early.
Technology is here today, right now, increasing revenues, decreasing costs, mitigating risk, solving manual repetitive tasks, and helping businesses compete more efficiently and effectively.
Three Big Conundrums for Today’s Credit Managers
Problem 1. Onboarding customers faster, with more visibility while simplifying the complex.
Streamlining existing customer onboarding processes requires an understanding of the full end to end process, from ‘I want to trade with you, how do I sign up?’ through to ‘My account is open, now what?’. You’re looking at around a dozen primary and secondary data sources to help you validate and make decisions about whether to extend credit or not. You may even be responsible for different business lines who all require variations of their own.
As finance experts you have to consider the channel your customer is coming through (web/rep/ store) and the degree of checking you’d like to do based on the sales channel they’re coming through. This needs to be done without impacting customer experience.
Then you also need different levels of checks based on the degree of risk you’re exposing yourself to, this helps you to manage costs.
It is such a big topic and requires a strategic lens for your individual businesses however mostly all answers lie in just three words: technology-enabled automation.
Technology exists today that enables you to solve and improve end-to-end customer problems. Self-serve, at the click of a button. ➤
We are not talking about expensive ERP-like installations here. We are talking, same day, ready to go SaaS solutions. No custom builds needed, little disruption and minimal cost.
SaaS (Software as a Service) applications are typically accessed by users using a web browser. Key benefits of SaaS products include: A. Accelerated delivery – you get multiple new improvements and features in quick succession, typically daily/weekly, generally with little to no disruption. B. Multi tenanted use of the product by multiple users, which drives cost efficiencies for all users. C. API (Application programming integration), affordable integration, managed and maintained for you. D. Maintenance, Training, Support – typically all of these costs are covered in your licensing fees. E. Latest technology – New and better ways of delivering superior experiences are rolling out daily. SaaS companies typically stay abreast of these and upgrade, most of the time without you even knowing. Streamlining a business using technology can significantly simplify the process, achieve digital transformation, increase service quality, improve data, improve service delivery, increase revenue and reduce costs.
Problem 2. As competition rises, improving customer XP for increased revenues
We all know that superior Customer Experience equates to increased sales, revenue and loyalty. According to Forbes, it brings in 5.7 times more revenue than competitors who lag in this space. What does ‘superior’ mean, and how do you measure it in your business?
Ask your customers what they think using NPS (Net promoter score) software which is cost effective and enables you to use data to make business decisions instead of hearsay or best guess. Net Promoter Score (NPS) is used to measure customer loyalty and how likely they are to refer your products and services to others. NPS helps identify who among your customers are promoters, passives, and detractors.
The feedback enables you to understand where and what in your workflow needs improving, all of which is achievable via technology.
Next is having the ability to create experiences tailored to your own business, from sales to finance to marketing, including streamlining your approval processes – a baseline requirement. So how can you achieve this without it costing the earth?
With the advent of AI,ML, OCR, and especially customisable interfaces and the many other technologies available, streamlining the user experience is not only achievable but it’s also expected.
All of these technologies typically feature and continue to evolve in most SaaS products ensuring you are always ahead of technological advancements.
Problem 3. Your need to identify your customer collides with data privacy
As Finance professionals, I don’t need to tell you what is required here. But what comes quickly to
especially SaaS will stay on top of this with you (I use the term ‘with you’ because creating technology is about the customer and SaaS provider working together in a valued partnership).
mind is – identification for fraud prevention and of course to bind agreements, legally binding contracts, data collection, data storage, data security, data ownership, credit risk profiles, identity risk profiles ...and the list goes on.
Staying on top of governance and legislation as regulation evolves especially around data, is going to be critical in order to remain relevant.
I’m not a lawyer (so please seek legal advice for clarity), but like you I know a thing or two about contracting. Let’s start with identifying your applicant and those key stakeholders involved in the contract. Here’s why this is important – a contract to be legally valid, as you probably know, requires an offer, acceptance, consideration, intent, together with certainty of terms and parties need to be legally capable of signing.
But how do you know the person signing the agreement is actually that person, and they are connected with the applicant entity?
Now add into the mix the rules around collecting, storage and usage of data. Data protection and privacy legislation is changing worldwide as more and more social and economic activities move online. Of most concern is sharing of personal information to third parties without notice or consent.
A good example of these changes are the GDPR regulations, which nearly all of us would have come across by now. In Australia, privacy principles are laid out clearly on the OAIC website (I encourage you to read them) and of note: A breach of an Australian Privacy Principle is an ‘interference with the privacy of an individual’ and can lead to regulatory action and penalties.
For us in Credit Management, there are many questions to ask and answer
What happens when your customer says: “Yes, you can view my drivers licence but you cannot store a copy of it” or “Yes, you can use this data supplied to approve my trade credit application, but it must not be shared” (for example, with credit bureaux).
The future state may be that customers gain visibility of where data is being requested (via NFTs) and have full control of that to approve and push it, or not as may be the case.
No doubt, customers will become more aware and vocal of their rights to data ownership, and we will need to stay ahead of that curve.
A good example of this is having the option to not store applicants and key signatories’ identifications, by wiping these from any database to protect your customers privacy and their individual right to do so.
With regard to data privacy and protection – technology again,
The use case of SaaS automation applied to identity fraud in trade credit
Let’s look at trade credit fraud incited by SMEs. There are over 2 million businesses in Australia, the majority being Small & Medium Enterprise (SME), the ratio of small to large is about 9:1, which is around the same ratio regardless of country around the world.
Whether you are a large or small business, chances are you
are doing business with a SME. The challenge is knowing which are disguised as fraudsters amongst them. This becomes even more of a challenge when credit applications are made online or through a channel that is more removed than face to face.
Think about it. Any person can register with ASIC, get an ABN and buy a legitimate looking website and logo for just a few hundred dollars. In many instances, that’s all they need to be eligible for trade credit. It could be anyone applying. The Australian Institute of Criminology (AIC) report of 2020 shows that a 33% increase in identity theft has been reported in one year. Complete 100-point identity packages of stolen or fabricated identities are for sale on all darknet markets for as little as a few hundred dollars (IDCARE 2020). AIC valued the financial impact of identity theft in Australia ➤
between $1.4bn and $2bn annually (Research report 15, 2018).
“There’s no doubt that identity fraud is hurting business.
As fraudsters become more sophisticated, business is turning to AI/ML and automation for the answers”.
Enter AI powered SaaS as your fraud investigator, credit checker and scam mitigator
Detecting and preventing fraud with artificial intelligence (AI) makes sense. It’s immensely scalable and increases in accuracy over time when used in conjunction with machine learning (ML) capability.
For context you can differentiate both AI and ML as: AI creates intelligent machines that can simulate human capability and behavior especially beneficial for those repeatable tasks, whereas ML is an application or subset of AI that allows machines to learn from data without being programmed explicitly.
AI can help prevent and detect suspicious activity especially where it comes to identifying the person you are doing business with. It will come as no surprise that this technology is not going anywhere anytime soon, infact will only evolve as its capability grows.
Let’s take an example.
Mark is a small business owner,
and he applies for trade credit. On paper, Mark checks out. However, as a Credit Manager, you don’t necessarily have access to all the information you need about Mark. You may do a 100 point identity check with an ASIC check and possibly a credit bureau check.
On the other hand, Robotic Process Automation will ingest Mark’s application – that could be online or using a QR code in-store. If needed, it will use optical character recognition (OCR) to read the application, recognise the text and complete Mark’s data set.
Then Machine Learning algorithms run checks across
large datasets automatically. From ASIC and Bank transactions to Government Licensing. It will automate facial recognition using biometrics and, in under 10 minutes, will predict whether Mark is a suitable applicant for trade credit. Not only is the process far quicker, but the magnitude of feedback signals makes it a far more robust process.
For other user stories download the 16 page eBook here, on this topic.
Machine learning models monitor, evaluate and detect red-flag anomalies predicting and prescribing the ‘next best action’
This means practically that when a borrower is deemed high risk, the algorithm you have determined may not automatically decline the application. Rather, it will convey the information to the Credit Manager to make a more informed decision.
Where credit amounts are small (under a set threshold), autodecisioning can be used. Here AI/ ML will predict the likelihood of fraud and, if it’s found to be low risk, can make the approval on your behalf.
Artificial intelligence as the Credit Manager’s co-pilot:
Automation and AI/ML will help you with these critical functions: z Predictive Analytics – Analytics on large integrated datasets which can detect suspicious behaviour correlated with past instances of fraud. z Anomaly Detection – Detecting deviations from normal activity
compared to historical data over a period that can be scaled differently for separate investigations. z Recommendations –
Recommending a “next best action” immediately following detection.
Inaccurate data increases the risk of identity theft. Hello tokenization.
The quality of customer identity data within your business is critical in reducing fraud and efficiently onboarding customers. Quality issues in identity data are plentiful. It could stem from insufficient or incorrect information collected during onboarding or because customer information (like addresses and phone numbers) changes over time. Then, try getting all your application channels – from sales reps to online applications – to be standardised, accurate and rationalised into one master data system. This fragmentation makes quality a more complex issue to solve.
Unfortunately, identity fraud increases with poor data, technology, and accessibility. This is where OCR technology, AI technology and even different types of facial recognition technology are available today for you to reduce this risk significantly. These technologies are a more scalable and more cost-efficient solution for problems created by poor quality in data. Goes without saying my money is on digital identity via NFT’s as referenced earlier.
Reducing the burden for your credit team and your customers
An undesirable consequence of mitigating identity fraud is a bad customer experience. This is manifested in either lengthy procedures while you manually work through your due diligence; or in setting the risk threshold so high that it results in low pass rates.
Furthermore, with risk mitigation as the aim, many strategies tighten authentication and make it more burdensome for customers. The outcome of which is a significant drop in revenues from approved customers.
Adopting automation solves this issue. You get the stringent checks, but the burden is on the machine, not your Credit Department nor the customer.
It’s better to be the fence at the top of the hill, making views (and dreams) possible rather than the ambulance at the bottom of it. Typically, businesses that adopt automation for trade credit see a 30% rise in safe revenue within the first month. That’s the business case for automation, right there. Evolution or revolution, the benefits of advanced technologies are immense for finance leaders.
*Miriana Lowrie CEO 1Centre E: miriana@1centre.com T: 64 021 705 060
