An open letter to American Express

Dear American Express,

I have been a loyal customer of yours for around 20 years. (Likewise my significant other.)

I typically pay my monthly statements on time and in full.

I’ve opted for paperless statements.

I pay my annual membership fee.

I even accept the fact that 7-8 times out of 10, I get charged merchant fees for paying by Amex – and in most cases I incur much higher fees than other credit or debit cards.

So, I am very surprised I have not been invited to attend your pop-up Open Air Cinema in Melbourne’s Yarra Park – especially as I live within walking distance.

It’s not like you don’t try to market other offers to me – mostly invitations to increase my credit limit, transfer outstanding balances from other credit cards, or “enjoy” lower interest rates on one-off purchases.

The lack of any offer in relation to the Open Air Cinema just confirms my suspicions that like most financial institutions, you do not really know your customers.

My point is, that you must have so much data on my spending patterns and preferences, from which you should be able to glean my interests such as film, the arts, and entertainment.

A perfect candidate for a pop-up cinema!

Next week: Life After the Royal Commission – Be Careful What You Wish For….

 

Big Data – Panacea or Pandemic?

You’ve probably heard that “data is the new oil” (but you just need to know where to drill?). Or alternatively, that the growing lakes of “Big Data” hold all the answers, but they don’t necessarily tell us which questions to ask. It feels like Big Data is the cure for everything, yet far from solving our problems, it is simply adding to our confusion.

Cartoon by Thierry Gregorious (Sourced from Flickr under Creative Commons – Some Rights Reserved)

There’s no doubt that customer, transaction, behavioral, geographic and demographic data points can be valuable for analysis and forecasting. When used appropriately, and in conjunction with relevant tools, this data can even throw up new insights. And when combined with contextual and psychometric analysis can give rise to whole new data-driven businesses.

Of course, we often use simple trend analysis to reveal underlying patterns and changes in behaviour. (“If you can’t measure it, you can’t manage it”). But the core issue is, what is this data actually telling us? For example, if the busiest time for online banking is during commuting hourswhat opportunities does this present? (Rather than, “how much more data can we generate from even more frequent data capture….”)

I get that companies want to know more about their customers so they can “understand” them, and anticipate their needs. Companies are putting more and more effort into analysing the data they already have, as well as tapping into even more sources of data, to create even more granular data models, all with the goal of improving customer experience. It’s just a shame that few companies have a really good single view of their customers, because often, data still sits in siloed operations and legacy business information systems.

There is also a risk, that by trying to enhance and further personalise the user experience, companies are raising their customers’ expectations to a level that cannot be fulfilled. Full customisation would ultimately mean creating products with a customer base of one. Plus customers will expect companies to really “know” them, to treat them as unique individuals with their own specific needs and preferences. Totally unrealistic, of course, because such solutions are mostly impossible to scale, and are largely unsustainable.

Next week: Startup Governance

 

Assessing Counterparty Risk post-GFC – some lessons for #FinTech

At the height of the GFC, banks, governments, regulators, investors and corporations were all struggling to assess the amount of credit risk that Lehman Brothers represented to global capital markets and financial systems. One of the key lessons learnt from the Lehman collapse was the need to take a very different approach to identifying, understanding and managing counterparty risk – a lesson which fintech startups would be well-advised to heed, but one which should also present new opportunities.

In Lehman’s case, the credit risk was not confined to the investment bank’s ability to meet its immediate and direct financial obligations. It extended to transactions, deals and businesses where Lehman and its myriad of subsidiaries in multiple jurisdictions provided a range of financial services – from liquidity support to asset management; from brokerage to clearing and settlement; from commodities trading to securities lending. The contagion risk represented by Lehman was therefore not just the value of debt and other obligations it issued in its own name, but also the exposures represented by the extensive network of transactions where Lehman was a counterparty – such as acting as guarantor, underwriter, credit insurer, collateral provider or reference entity.

Before the GFC

Counterparty risk was seen purely as a form of bilateral risk. It related to single transactions or exposures. It was mainly limited to hedging and derivative positions. It was confined to banks, brokers and OTC market participants. In particular, the use of credit default swaps (CDS) to insure against the risk of an obiligor (borrower or bond issuer) failing to meet its obligations in full and on time.

The problem is that there is no limit to the amount of credit “protection” policies that can be written against a single default, much like the value of stock futures and options contracts being written in the derivatives markets can outstrip the value of the underlying equities. This results in what is euphemistically called market “overhang”, where the total face value of derivative instruments trading in the market far exceeds the value of the underlying securities.

As a consequence of the GFC, global markets and regulators undertook a delicate process of “compression”, to unwind the outstanding CDS positions back to their core underlying obligations, thereby averting a further credit squeeze as liquidity is released back into the market.

Post-GFC

Counterparty risk is now multi-dimensional. Exposures are complex and inter-related. It can apply to any credit-related obligation (loans, stored value cards, trade finance, supply chains etc.). It is not just a problem for banks, brokers and intermediaries. Corporate treasurers and CFOs are having to develop counterparty risk policies and procedures (e.g., managing individual bank lines of credit or reconciling supplier/customer trading terms).

It has also drawn attention to other factors for determining counterparty credit risk, beyond the nature and amount of the financial exposure, including:

  • Bank counterparty risk – borrowers and depositors both need to be reassured that their banks can continue to operate if there is any sort of credit event or market disruption. (During the GFC, some customers distributed their deposits among several banks – to diversify their bank risk, and to bring individual deposits within the scope of government-backed deposit guarantees)
  • Shareholder risk – companies like to diversify their share registry, by having a broad investor base; but, if stock markets are volatile, some shareholders are more likely to sell off their shares (e.g., overseas investors and retail investors) which impacts the market cap value when share prices fall
  • Concentration risk – in the past, concentration risk was mostly viewed from a portfolio perspective, and with reference to single name or sector exposures. Now, concentration risk has to be managed across a combination of attributes (geographic, industry, supply chain etc.)

Implications for Counterparty Risk Management

Since the GFC, market participants need to have better access to more appropriate data, and the ability to interrogate and interpret the data, for “hidden” or indirect exposures. For example, if your company is exporting to, say Greece, and you are relying on your customers’ local banks to provide credit guarantees, how confidant are you that the overseas bank will be able to step in if your client defaults on the payment?

Counterparty data is not always configured to easily uncover potential or actual risks, because the data is held in silos (by transactions, products, clients etc.) and not organized holistically (e.g., a single view of a customer by accounts, products and transactions, and their related parties such as subsidiaries, parent companies or even their banks).

Business transformation projects designed to improve processes and reduce risk tend to be led by IT or Change Management teams, where data is often an afterthought. Even where there is a focus on data management, the data governance is not rigorous and lacks structure, standards, stewardship and QA.

Typical vendor solutions for managing counterparty risk tend to be disproportionately expensive or take an “all or nothing” approach (i.e., enterprise solutions that favour a one-size-fits-all solution). Opportunities to secure incremental improvements are overlooked in favour of “big bang” outcomes.

Finally, solutions may already exist in-house, but it requires better deployment of available data and systems to realize the benefits (e.g., by getting the CRM to “talk to” the loan portfolio).

Opportunities for Fintech

The key lesson for fintech in managing counterparty risk is that more data, and more transparent data, should make it easier to identify potential problems. Since many fintech startups are taking advantage of better access to, and improved availability of, customer and transactional data to develop their risk-calculation algorithms, this should help them flag issues such as possible credit events before they arise.

Fintech startups are less hamstrung by legacy systems (e.g., some banks still run COBOL on their core systems), and can develop more flexible solutions that are better suited to the way customers interact with their banks. As an example, the proportion of customers who only transact via mobile banking is rapidly growing, which places different demands on banking infrastructure. More customers are expected to conduct all their other financial business (insurance, investing, financial planning, wealth management, superannuation) via mobile solutions that give them a consolidated view of their finances within a single point of access.

However, while all the additional “big data” coming from e-commerce, mobile banking, payment apps and digital wallets represents a valuable resource, if not used wisely, it’s just another data lake that is hard to fathom. The transactional and customer data still needs to be structured, tagged and identified so that it can be interpreted and analysed effectively.

The role of Legal Entity Identifiers in Counterparty Risk

In the case of Lehman Brothers, the challenge in working out which subsidiary was responsible for a specific debt in a particular jurisdiction was mainly due to the lack of formal identification of each legal entity that was party to a transaction. Simply knowing the counterparty was “Lehman” was not precise or accurate enough.

As a result of the GFC, financial markets and regulators agreed on the need for a standard system of unique identifiers for each and every market participant, regardless of their market roles. Hence the assignment of Legal Entity Identifiers (LEI) to all entities that engage in financial transactions, especially cross-border.

To date, nearly 400,000 LEIs have been issued globally by the national and regional Local Operating Units (LOU – for Australia, this is APIR). There is still a long way to go to assign LEIs to every legal entity that conducts any sort of financial transaction, because the use of LEIs has not yet been universally mandated, and is only a requirement for certain financial reporting purposes (for example, in Australia, in theory the identifier would be extended to all self-managed superannuation funds because they buy and sell securities, and they are subject to regulation and reporting requirements by the ATO).

The irony is that while LEIs are not yet universal, financial institutions are having to conduct more intensive and more frequent KYC, AML and CTF checks – something that would no doubt be a lot easier and a lot cheaper by reference to a standard counterparty identifier such as the LEI. Hopefully, an enterprising fintech startup is on the case.

Next week: Sharing the love – tips from #startup founders

#FinTech – using data to disintermediate banks?

At a recent #FinTechMelb meetup event, Aris Allegos, co-founder and CEO of Moula, talked about how the on-line SME lender had raised $30m in investor funding from Liberty Financial within 9 months of launch, as evidence that their concept worked. In addition, Moula has access to warehouse financing facilities to underwrite unsecured loans of up to $100k, and has strategic partnerships with Xero (cloud accounting software) and Tyro (payments platform).

Screen Shot 2015-09-07 at 10.52.16 amMoula is yet one more example of how #FinTech startups are using a combination of “big data” (and proprietary algorithms) to disrupt and disintermediate traditional bank lending, both personal and business. Initially, Moula is drawing on e-commerce and social media data (sales volumes, account transactions, customer feedback, etc.). Combined with the borrower’s cashflow and accounting data, plus its own “secret sauce” credit analysis, Moula is able to process on-line loan applications within minutes, rather than the usual days or weeks that banks can take to approve SME loans – and the latter often require some form of security, such as property or other assets.

So far, in the peer-to-peer (P2P) market there are about half-a-dozen providers, across personal and business loans, offering secured and unsecured products, to either retail or sophisticated investors, via direct matching or pooled lending solutions. Along with Moula, the likes of SocietyOne, RateSetter, DirectMoney, Spotcap, ThinCats and the forthcoming MoneyPlace are all vying for a share of the roughly $90bn personal loan and $400bn commercial loan market, the bulk of which is serviced by Australia’s traditional banks. (Although no doubt the latter are waking up to this threat, with Westpac, for example, investing in SocietyOne.)

We should be careful to distinguish between the P2P market and the raft of so-called “payday” lenders, who lend direct to consumers, often at much higher interest rates than either bank loans or standard credit cards, and who have recently leveraged web and mobile technology to bring new brands and products to market. Amid broad allegations of predatory lending practices, exorbitant interest rates and specific cases of unconscionable conduct, payday lenders are facing something of a backlash as some banks decide to withdraw their funding support from such providers.

However, opportunities to disintermediate banks from their traditional areas of business is not confined to personal and business loans: point-to-point payment services, stored-value apps, point of sale platforms and foreign currency tools are just some of the disruptive and data-driven startup solutions to emerge. That’s not to say that the banks themselves are not joining in, either through strategic partnerships, direct investments or in-house innovation – as well as launching on-line brands, expanded mobile banking apps and new product distribution models.

But what about the data? In Australia, a recent report from Roy Morgan Research reveals that we are increasingly using solely our mobile devices to access banking services (albeit at a low overall engagement level). But expect this usage to really take off when ApplePay comes to the market. Various public bodies are also embracing the hackathon spirit to open up (limited) access to their data to see what new and innovative client solutions developers and designers can come up with. Added to this is the positive consumer credit reporting regime which means more data sources can be used for personal credit scoring, and to provide even more detailed profiles about customers.

As one seasoned banker told me recently as he outlined his vision for a new startup bank, one of the “five C’s of credit” is Character (the others being Capacity – ability to pay based on cashflow and interest coverage; Capital – how much the borrower is willing to contribute/risk; Collateral – what assets can be secured against the loan; and Conditions – the purpose of the loan, the market environment, and loan terms). “Character” is not simply “my word is my bond”, but takes into account reputation, integrity and relationships – and increasingly this data is easily discoverable via social media monitoring and search tools. It stills needs to be validated, but using cross-referencing and triangulation techniques, it’s not that difficult to build up a risk profile that is not wholly reliant on bank account data or payment records.

Imagine a scenario where your academic records, club memberships, professional qualifications, social media profiles and LinkedIn account could say more about you and your potential creditworthiness than how much money you have in your bank account, or how much you spend on your credit card.

Declaration of interest: The author currently consults to Roy Morgan Research. These comments are made in a personal capacity.

Next week: Rapid-fire pitching competitions hot up…..