By Greg Michaelson, VP, General Manager of Banking, DataRobot
Banks have been using machine learning longer than pretty much anyone else, and for good reason. Identifying the lowest risk borrowers, pricing loans, and forecasting losses all require the use of predictive models. Sometimes these models are simple scorecards. Other times, they are highly complex machine learning models. Many banks have at least some of these core models built, with a process in place for monitoring and maintaining them, but outside the handful of core models, the need for embedding artificial intelligence (AI) and machine learning at a deeper level is an unfulfilled need at most banks.
At least part of this deficiency in AI and machine learning can be explained by the capacity of data scientists and availability of data. Traditionally, data science teams have had their hands full building and maintaining the core models of the bank. Add to that the challenges involved with managing, cataloging, and assembling data, along with the obstacles associated with implementing these models into production, and it’s no wonder that progress has been so slow.
To complicate matters, the number of players in the banking sector has increased dramatically in the last several years. Fintech companies are hard at work gobbling up bits of market share, and they’re doing it by means of innovation and AI. Banks still have the advantage in terms of data and expertise, but that advantage won’t last if bankers don’t start innovating.
When it comes to AI, some of the lowest hanging fruit in banks today are the so-called relationship businesses. These are typically high-touch, high-profit, complex relationships that have dedicated relationship managers and generate a mountain of revenue for banks. Business banking and corporate banking businesses along with wealth management teams are good examples of this type of relationship business.
Several years ago, I was at a sale offsite for a Fortune 100 company. The attendees were relationship managers that were all compensated on sales. The first half of the day was set aside for discussing prospecting strategies. Salesperson after salesperson stood up and told about the newest event that they had hosted that was really “working” for them. Box seats at sporting events, breakfast meetings, cold calling, and many other in-person events were touted as “the way” that was going to produce results.
At the time, I remember being struck by how unscientific it was. I even made a comment about collecting data to see what was actually working. I was quickly reprimanded: “This is a relationship business,” one experienced salesman told me.
I’ve heard that same sentiment many times since then. “This loan is too complex for a pricing model.” “This relationship is too unique to be lumped into a dataset and learned from.” “This account is much too important to be driven by your models.”
Fast forward a few years, and these relationship-driven businesses are beginning to change their tune. Businesses, who are impacted by ultra-low interest rates and increased competition, are now open to exploring new strategies to grow and deepen their relationships. The opportunity to do so is huge.
The use cases
All sales teams use CRM tools to track leads, opportunities, and accounts. That means that all sales teams have incredibly valuable data that tracks where they’ve been successful and where they haven’t. This data is the foundation of building an AI solution to grow a business.
Prospecting for the best new customers
In the world of sales, prospect lists abound, driven by inbound leads, cold calls, and third-party data sources. Sorting through it all to know who to contact, and who to focus on, can be bewildering — particularly if you don’t have access to any reporting or analysis showing what has been effective (and what hasn’t).
Building an AI solution for prospecting involves first identifying what a “good” prospect looks like. The most obvious definition of a good prospect is anyone that is likely to buy what you’re selling, but that’s far from the only example. Bankers might want to try to predict the credit quality of prospects based on publicly available data to identify the clients where they want to spend their energy. Or perhaps they might want to predict which clients are likely to produce the most value for their business, either by growing in the future or by their needs for a wide variety of products.
In any case, once a good customer is defined, then it’s just a matter of matching up external prospect data with a bank’s existing client base to build a training dataset. For example, I might take the data I have available for my prospects (my features) and match them up to my current clients where I know the target value; e.g., risk rating or profitability (my prediction target). I might try to predict profitability, product appetite, or margin. Once the models are built, I can then score my list of prospects in order to rank them for potential sales efforts.
Deepening relationships with your current customers
The number of products and services that a customer needs is strongly correlated with how profitable that customer is. AI can improve the effectiveness of relationship managers by identifying particular customers that are in need of particular products. If a relationship manager can reach out with a specific product to the right customer (and not reach out with the wrong products), then that customer will be happier, sales will be higher, and the relationship will grow stronger.
Fortunately, if the bank utilises a CRM system, the data needed for this project has probably already been captured. It’s a simple matter of identifying which customers were offered which products and whether or not they bought them. Which customer and product attributes will be predictive of purchasing likelihood will depend on the product.
Once I have my models in place, it’s relatively straightforward to identify the best prospects for a marketing campaign or to have an offer in the “back-pocket” for every customer interaction.
Pitfalls to avoid
These are just a couple of examples of ways that AI can increase the effectiveness of sales teams. Building the solutions is not complex from a technical perspective and deploying them is also pretty straightforward. That said there are a few blockers that you will want to avoid as you build these solutions.
Don’t fail to record information about lost sales. Salespeople hate data entry (doesn’t everyone?). They especially hate entering data about deals that they’ve lost. Capturing data about lost deals, though, is just as important as capturing data about won deals. Machine learning requires data that the machine can learn from. That means positive and negative examples. In order to build models to predict customer behavior, all the possible outcomes have to be captured.
Don’t wait until your data is perfect. Everyone has data issues, but it’s pretty rare to come across a data set-up that is so bad that nothing can be done. Starting with the data that’s currently available is the only way to get started with AI. Waiting until everything is perfect means never getting started at all.
Don’t expect it to work perfectly the first time. These solutions, even though straightforward, take some iteration. I worked with a sales team at a large bank to predict which clients were most likely to need a foreign exchange (FX) product to lock in conversion rates for cross-border payments. It took us six iterations to get to a good model, but when we finally did, we increased the conversion rate from 2% to 10%, which meant a $10M+ increase in sales for the organisation.
Prospecting and deepening models are two ways most banks can start exploring how AI can impact a your organisation. These use cases are low risk, quick to build, easy to implement, and have a high ROI, and advanced tools like automated machine learning now make developing these solutions accessible without the huge upfront cost required in the past; e.g., massive time investment, hiring and retaining large data science teams, tricky manual deployments, etc. Whether you’re a large bank or a credit union, with sizeable complex deals or simple term loans, AI can provide a straightforward way to be more targeted in your sales efforts.
Bank fraud prevention in a post-COVID-19 world
By Pierre-Antoine Dusoulier, Founder and CEO, iBanFirst
Fraud on the rise
According to recent research from a leading UK retail bank, there was a 66 per cent increase in reported scams in the first six months of 2020 compared with the last six months of 2019 – due to the COVID-19 pandemic.
Across the summer months, Action Fraud UK reported a total financial loss of £11,316,266 by 2,866 victims of coronavirus-related scams.
The rise in fraud rates is a warning that banks, building societies and other financial providers need to be as alert as ever in identifying fraud.
So, what do banks need to do to ensure their customers are protected from fraud in a post-COVID-19 world?
Educate your customers to safeguard against fraud
On the customer level, banks need to be informing their customers on the types of common fraud to ensure that they are protected for all eventualities.
Authorised push payment scams are one of the fastest growing types of fraud. According to the FT, £354 million pounds was stolen this way last year. It is where a company or individual is tricked into paying money into a criminal’s account. Emails come from a genuine email address but are then intercepted by a criminal, so it’s imperative that businesses have end-to-end email encryption, and the customer double-checks the account details with the supplier on the phone prior to making a payment.
At the same time, scammers can also exploit the company’s invoicing process, where criminals create a bogus invoice for a small amount and send it to a company’s accounting department. If the finance team does not identify this as fraudulent, it can result in the business losing a considerable amount of revenue over a long period of time.
Supplier fraud is also a widespread scam. This involves the fraudster taking on the appearance of a supplier that has changed their bank details. The fraudster will have collected information on the suppliers of the targeted company, in order to pose as an official supplier. This can be prevented by ensuring that the supplier is contacted to confirm the legitimacy of the communication. It’s important not to call or email the supplier using the details provided on the suspected fraudulent correspondence. Instead they must check the original details of the supplier and speak to them on their official telephone number or email on file.
Banking malware is the least commonly cited type of fraud but has a greater financial risk attached to it. Malware is sent by email redirecting the recipients of the message to a fake banking interface, as a way of transferring funds to offshore accounts.
Remodel processes post-COVID-19 to keep customer data safe
To fight cyber fraud and scams, banks must also play their part. In a world where entire workforces are working from home banks must remain vigilant with customer data. COVID-19 has created a change in working habits and banks need to carry out the right level of training for its employees to protect customer data. Virtual team meetings and remote data sharing poses a threat to exposing sensitive information to malicious actors, and banks need to put the necessary safeguards in place.
All virtual meetings should use the banks’ private company network, and file sharing should be carried out through secure, encrypted company drives. Meanwhile, banks need to provision for all employees to receive regular software updates that will keep customer data safe, and ensure that they are aligned with new and existing data processing regulations.
Monitoring suspicious payments
A vital element to fraud detection is through monitoring customer transactions in real time, and harnessing emerging technologies such as artificial intelligence and machine learning to spot the signs of a scam or fraud before it is too late.
One way that banks protect businesses from fraud is through keeping a log and examining regular transactional history. Any transactions which appear suspicious based on location, amount, the beneficiary, and the method will be alerted to the business customer, to mitigate the immediate and future financial risk to the business.
Know your transaction
To understand financial flows better, every bank has a Know Your Customer (KYC) engine. This is a payment infrastructure that supports onboarding processes and risk-based transaction monitoring. This system is already well known and we don’t need to elaborate on this further, as it is the fundamental building block to ensure the highest level of traceability across all transactions – including remittances and receipts of funds and foreign exchange transactions internationally.
However, KYC is limited and doesn’t include real-time analysis. What can be overlooked is a KYT engine – Know your Transaction. The aim of KYT (Know Your Transactions) is to identify potentially risky transactions and their underlying unusual behaviour for detecting money laundering, fraud or corruption. An automated concentration of transactions with accurate and relevant information directly from the original data sources is essential.
Finally, banks and payment companies need to implement anti-fraud modules to defend against cyberattacks, based on the latest algorithms capable of analysing transactions issued in real time and detecting anomalies or suspicious behaviour upstream, strengthening the security and transparency of payments and building a network of trust between issuers and recipients of payments.
In a post-COVID-19 world it’s clear that scams will become more common place. Within this environment there is a shared responsibility when mitigating the risk of financial fraud. The bank must educate and inform customers to enable them to protect themselves, while ensuring a robust technological infrastructure and ways of working are in place that protects customer data; their finances, and fundamentally their business and livelihood.
How One Bank Successfully Responds to Sophisticated Threat Actors
By Robert Golladay, Strategic Accounts Director, Illusive Networks
Cybercriminals and hacktivists have a special fondness for financial institutions. Continuous business innovation, complex ecosystems, merger and acquisition activity, fintech, cloud adoption and a growing consumer-driven attack surface multiply the problem for financial organizations. Despite the vast resources financial institutions devote to cybersecurity, one challenge has been especially difficult to solve – that of detecting and stopping APTs before real damage is done.
Securing cloud-based banking
An active lender in the UK sought a new way to protect its customers and the valuable assets it holds. The bank needed to:
- Defend customer and employee information from compromise
- Detect and thwart sophisticated attacks
- Effectively defend cloud-based operations across accounts and instances
As a cloud-first company, the bank’s preference is to always invest in next-generation technology for operations and security infrastructure. In May 2016, with the help of Amazon Web Services (AWS), it became the first bank in the UK to be fully cloud hosted. The bank also uses AWS to deliver a financial technology service that helps lenders make informed decisions through data and automation.
Security is always a priority, which is one of the reasons the company chose AWS, conducts regular penetration testing, and performs advanced attack simulations. To maximize effectiveness of its layered security infrastructure, the company continually trains its employees and reinforces data security best practices.
In particular, the bank sought additional safeguards from sophisticated threats that evade other security measures, such as advanced persistent threats, as well as gain insight into attacker tactics and techniques. The new layer needed to be cloud-based for high scalability and flexibility, and it had to defend the company without time-wasting false positive alerts. The security team looked at deception technology and chose a solution that allowed them to gain real-time verification of anomalies and lateral movement in the network.
The deception solution enabled the bank to focus on attackers’ behaviour and perspective. The solution’s expertise in attacker methodology augmented the bank’s internal capability to detect novel attacks, while enabling rapid and adaptable coverage in its cloud-based environment.
The bank’s deception solution uses agentless, intelligence-driven technology that creates a dense web of deceptions and effortlessly scales across the infrastructure. Featherweight deceptions on every endpoint look exactly like the bank’s real data, access credentials and connections. When an attacker is confronted with deceptions, this deceptive view of reality makes it impossible to choose a real path forward. One wrong step triggers an alert to the bank’s security team.
The bank’s CISO found it invaluable to be able to deploy a solution that creates doubt and confusion in an intruder’s mind. When attackers can’t distinguish between real and deceptive assets, the security team can collect information and apply intelligence to patterns that it has observed during that time period of activity. The solution simultaneously sharpens the bank’s investigative process and constrain the attacker.
The lender easily deployed deception technology across its complex environment, scaling it across AWS instances and accounts. The IT security team now has continuous visibility and confidence that these defences enable them to thwart sophisticated threat actors.
The bank gained proactive threat response and the assurance that an alert represents a real issue. These alerts are only triggered when an attacker engages with a deceptive asset. At that point, the deception technology immediately begins capturing forensic data from the system where the attacker is operating, presenting real-time forensics and a quantifiable measure of potential business risk. It uncovered, for example, malicious processes trying to operate on an endpoint.
The deception solution enables the lender to be much more proactive. It detects and analyses attacks in real time to produce actionable alerts, directing the security team to relevant and valuable conclusions. The technology provides exceptional, innovative coverage for malicious pivoting and lateral movement. It uncovers the in-depth, sophisticated actors who evade other countermeasures and gives security analysts direct visibility into targeted attacks, which they find invaluable.
A laser-focused approach
The financial sector remains a perennial favourite of the cybercriminal crowd. As networks become more complex, their perimeters all but disappear, creating the need for stronger and more comprehensive security than ever previously imagined. Advanced persistent threats are a particular concern, as they are notoriously difficult to detect before significant damage is done. For financial institutions, the reputation damage alone may be insurmountable.
Banks and other financial services organizations pour resources into cybersecurity, but one option that needs further exploration is deception technology. This method of security monitors for lateral movements toward critical assets and thus provides a powerful alternative or enhancement to traditional monitoring approaches. Security teams can see attackers’ proximity to those crown jewels early in the attack cycle, buying time for careful response. As the lender above learned, deception technology cuts through the noise of alerts to deliver the intel financial institutions need to act quickly and safeguard their high-value data.
Why banking and finance need to move qualifications online
By Rory McCorkle, Senior Vice President, PSI Certification and Education Services
The global banking and finance sector often presents a strange contradiction when it comes to technology. On one hand, the sector is leading the way in blockchain technology, big data and Artificial Intelligence. On the other hand, many large financial institutions are falling behind in their digital transformation efforts, with internal processes as well as the moving the customer experience online. Particularly when compared to fintech and new challenger banks.
A report last year by Accenture found that just 12% of large traditional banks surveyed have fully committed to digital transformation and 50% of banks made little progress. The remaining 38% are in the midst of their transformations, but their digital strategies lack coherence.[i]
One area of digital transformation that has been particularly slow is access to qualifications and certifications. Many exams in the banking and finance sector continue to use Paper Based Testing (PBT). However, COVID-19 has accelerated the transition from PBT to Computer Based Testing (CBT), proving irrevocably that change is possible – regardless of the size of your organisation, number of candidates or security requirements.
In a heavily regulated environment that is undergoing increased scrutiny, a high level of certification and compliance is a necessity for many working in the industry. And credentials that hold such significance need to be securely and fairly assessed. This is where CBT offers numerous benefits. For organisations there is security, integrity, flexible capacity, increased reach and a streamlined exam administration process. And for candidates, CBT provides flexibility, convenience, accessibility and increased choice.
Despite these benefits, some organisations still have reservations and have been slower to make the move to CBT. In more traditional professions, such as finance, there can be a greater reticence. This is likely to be based on the historic prestige of PBT, as well as a desire to stick to more traditional methods. However, with more learning completed online, and educational resources shifting to digital from primary education to CPD, expectations around assessments are changing.
Up-and-coming candidates in all professions, particularly those who are digital natives, are starting to question outdated methods. Organizations will need to adapt to stay current and relevant with their market. What’s more, technological advances have now combined with the coronavirus pandemic to increase the demand for remote business services. Meaning that a growing number of organisations in the banking and finance sector are moving to CBT.
Technology offers burgeoning options to increase test security with CBT. Linear-on-the-fly testing (LOFT) for example allows you to easily change items for each candidate, while maintaining the fairness of the exam – rather than the fixed forms used in PBT.
With LOFT, every candidate is given a unique set of items, making cheating a lot more difficult. And with no need to ship test papers around the country, there’s significantly less risk of physical security breaches with CBT than with PBT.
With the movement away from paper and pencil testing, advances in online proctoring have also dramatically increased the ability to deliver secure online assessments. Using a webcam and microphone, online proctoring provides test security for exams, while offering candidates additional flexibility and convenient scheduling.
Even before COVID-19, online proctoring was becoming far more commonplace. In 2018, there was a 10% increase in organisations using online proctoring with video/sound recording and identity authentication as part of the exam process compared to 2017.[ii] And COVID-19 has reinforced the fact that it is possible to effectively move to CBT side by side with online proctoring – and move quickly.
Testing has changed a lot during its history but the reasons for adopting CBT have remained the same for decades – fair and reliable testing delivered at scale. Nearly all tests that are completed with a paper and pencil can be adapted for CBT.
For organisations in the banking and finance sector, recent technological advances have provided many more options to reach candidates. At the same time, technology has significantly increased the security for important online assessments that will not only affect a candidate’s future, but might also impact the future and reputation of their profession.
As with any change, the move from PBT to CBT must be managed carefully and communicated clearly. And with best practice in place, it is possible for any organization, regardless of size and number of candidates, to make the move to CBT.
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