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FROM MANUAL TO MACHINE LEARNING: HOW TO APPROACH THE RECONCILIATION ‘PROBLEM’

By Christian Nentwich, CEO at Duco

 

At the start of 2020, before the global coronavirus pandemic changed the world, financial industry experts recognised that this would become the ‘decade of data’, with firms inundated with trillions of lines of data from a multitude of sources.

One of the many effects the current crisis has had is to amplify the need for resilient, connected systems and more robust processes. With business continuity front of mind, many organisations are looking for more efficient ways to manage huge swathes of data from multiple, disparate sources quickly and accurately. Data integrity is a key concern, and many are asking how they can automate their most critical processes.

However, despite the rush to digitalise many manual systems, automating reconciliations is still one of the toughest areas to crack.  Even pre-pandemic, automating this essential control function in financial services – which can help eliminate operational risk that can lead to fraud, fines, or in the worst case, the failure of a firm – was proving elusive for many organisations. Why?

Many organisations are facing a situation where there are a multitude of systems, different processes, technology types and computing.  Within that, there are three key reasons that make automation difficult:

  • A lack of standardisation – In many cases in financial services there are no strict data standards. For example, different counterparties provide trade and position data in different formats. Each one requires a bespoke reconciliation process or expensive data normalisation.
  • Increased complexity – Cash or stock assets can be matched on a few basic fields, but for more complex products you need to take far more information into account. Most current systems are unable to deal with every asset type that crops up in a timely manner. And, that’s before we get to the range of data needed for regulatory reporting, and the associated reconciliations required.
  • Poor data quality – The enemy of automation. Missing fields, inconsistent coding schemes and unavailability of common keys make automation difficult when using current solutions due to hardcoded assumptions within those systems.

However, in a world where the quantity and complexity of data that firms need to handle is set to increase exponentially, relying on manual systems and processes is no longer feasible. So, how do firms deal with this influx of data in the most intelligent way?

We recently launched ‘The Reconciliation Maturity Model’, a new roadmap that will help financial firms improve the automation, efficiency and integrity of data across all reconciliation and data matching tasks.  The model guides reconciliation practitioners through five key stages of reconciliation maturity, from ‘manual’ through to ‘automated’ and eventually ‘self-optimising’ – where machine-learning technology automates nearly the entire process, and where intersystem reconciliations are all but eliminated

Importantly, a more progressive approach to reconciliation automation will not only result in greater operational efficiency, it will also dramatically boost operational resilience, and put forward-thinking financial institutions in a better position to benefit from new technology and the added insight that intelligent systems bring.

The five stages of reconciliation maturity are:

  1. Manual – By this we mean using Excel or some other form of spreadsheet, macros, home-grown applications or – in some instances we’ve come across – printing out sheets of paper and marking inconsistencies with a highlighter pen! However, as the organisation grows, and the data becomes more complex, the risk of error skyrockets. There’s no audit trail, no governance and it becomes increasingly expensive to scale. If in the 2020s you’re throwing an increasing number of bodies at a data matching exercise, you know something’s wrong.
  2.  Hybrid – For the majority of organisations, this takes the form of a point solution, usually deployed to automate high volume, low complexity reconciliations such as cash or custody. These point solutions – by their very nature – tend to specialise in a certain type of reconciliation. Firms trading a wide range of assets, or those dealing with complex data, may need to use multiple point solutions to handle different reconciliation types. However, there will be many reconciliations that these point solutions are not able to handle elegantly. In these cases, firms tend to fall back on manual processes. The result is a patchwork quilt of different reconciliation approaches stitched together by manual work. The whole process is costly, difficult to keep track of, and difficult to scale.
  3.  Automated – All reconciliations are consolidated onto automated systems, and small teams build and onboard reconciliations, and oversee exception investigation.The key to getting to this stage is using the right technology. To reach Stage 3, firms need to be able to onboard reconciliations in hours or days, not weeks or months. They need to be able to rely on agile, flexible technology that can deal with complexity without multi-week data transformation projects. Once this technology is in place, complexity and risk can be vastly reduced, while increasing efficiency and transparency across processes.
  4. Improving – This enables greater efficiency and oversight of the reconciliation function as a whole. It also enables firms to normalise their data across the business and implement additional data quality checks across systems, highlighting areas of incomplete or incorrect data.  Organisations are then able to start consolidating systems and removing duplicate reconciliations which have already been handled upstream.  Processes become leaner, more efficient and more transparent.
  5. Self-optimising – Full automation is deployed across the entire lifecycle of reconciliation, from onboarding to exception resolution. There is very little involvement from staff and continuous improvement is possible via a machine-learning enhanced system. Internal reconciliations are removed, leading to major reduction in cost and complexity.

While stage five is the ‘holy grail’ that all financial organisations should be aspiring to, many firms are at the ‘hybrid’ stage, and making the leap to ‘automated’ is the most challenging step.  However, once at stage three, firms are more able to move up the process to ‘self-optimising’.  At this point, with enough training data, machine learning can spot errors, outliers and poor data quality at source, reducing the number of reconciliations required.

So, while we know that moving from manual to machine learning is not an overnight process, The Reconciliation Maturity Model provides a blueprint to getting there.

The Reconciliation Maturity Model is available for download here https://content.du.co/reconciliation-maturity-model-whitepaper

 

Business

CAN TECHNICAL INNOVATION HELP FINANCIAL SERVICES FIGHT BACK AGAINST FINANCIAL CRIME?

By Charlie Roberts, Head of Business Development, UK, Ireland & EU at IDnow

 

It’s no secret that the financial services sector is a top target among cyber criminals. In fact, according to a report from IBM, it retained its top spot as the most targeted sector in 2019.

The consequences of falling victim to an attack can be severe too. It can lead to financial losses and reputational damage as well as loss of customer confidence and therefore sales. One UK financial services firm, for example, was hit by a total loss of $87.9 million.

So, if we consider that the coronavirus crisis continues to drive increased online consumer activity, should financial services be more concerned? Simply put, yes.

We are seeing a significant increase in organisations taking their business online to reach their customers. Banks, for example, in adapting to COVID-19, are offering customers a more convenient way of opening an account given branch visiting restrictions. But while these services offer more choice and ease for customers, it also means that new account fraud is opening up and is becoming a major challenge for organisations to overcome.

Charlie Roberts

Some cyber criminals are even trying to exploit the pandemic as an opportunity for financial crime by posing as trusted organisations like banks and even the World Health Organisation. According to Action Fraud, over £6.2 million has reportedly been lost by UK citizens to coronavirus-related scams. And this figure continues to rise week by week.

 

The role of innovation

The rise in financial crime shows just how much the financial services sector is in need of technological innovation. We’ve already seen great progress. About half of financial services and insurance firms globally already use Artificial Intelligence (AI), according to Forrester.

It has many use cases too. In a recent report published by The Alan Turing Institute, AI is largely being used for fraud detection and compliance. AI is beneficial because its algorithms can analyse millions of data points to detect fraudulent transactions which could otherwise go unnoticed by humans. What’s more, these AI-driven fraud detection systems can now actively learn and calibrate in response to new potential (or real) security threats.

The report also details some of the ways that financial services companies are exploring AI-based fraud prevention alternatives. It includes the use of AI to increase approvals for genuine transactions and the use of real-time and high volume data to help protect schemes, financial institutions and their customers from fraud and financial crime.

It’s perhaps no wonder that, outside of the technology sector, the financial services industry is the biggest spender on AI services according to The Bank of the Future report from Citi. But there is still some way to go in using technology to combat financial crime.

 

The identity verification era

Arguably, identity verification is one of the most important processes that technology can help transform – especially as the current crisis continues to drive increased online customer behaviour. In fact, AI and video based identity verification software can provide financial services organisations with a fast, seamless and secure onboarding process that increases conversion rates and customer satisfaction while providing the highest level of security.

Demand for this software in the UK’s financial services sector has already more than doubled since the start of the year, as growth in scams linked to COVID-19 continue to rise.

It’s this technology that will become critical in validating a person’s identity quickly and confidently while limiting the increased risk of fraud for both businesses and consumers.

IDnow’s AutoIdent is one software solution that has this year been experiencing high demand from the financial services industry. Its AI technology can use the camera on a customer’s smartphone to recognise the country and type of ID document without the need for user input. The technology then captures the machine-readable part of the ID document as well as non-machine-readable areas, such as address fields, before automatically checking the optical security features of the ID documents, such as holograms.

With the subsequent biometric video check of the person and “liveness detection”, the identification process is completed for the customer within just a few steps. The system can then decide if the identification is valid, with a reliability that meets compliance requirements.

 

Fighting back

The threat of financial crime is not going away any time soon and so there is no better way than to fight back with innovation. With the right technology investment, such as in AI identity products, the sector will be in a stronger position to support businesses who have a duty of care to protect their customers from risk of fraud while ensuring they remain resilient during this pandemic.

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Business

TOP 5 LINKEDIN PROFILE OPTIMIZATION HACKS FOR ASPIRING BANKERS

According to Firmex, finance professionals cannot afford to be not on LinkedIn. A significant number of organizations acquire talent in the financial industry through LinkedIn.

Especially for aspiring professionals, your internet presence matters a lot as recruiters are most likely to search your name on the internet before making a decision about your application.

As an aspiring banker on a professional platform, you should consider changing the outlook of your profile, to garner the recruiter’s attention. Your profile is unlikely to get noticed if it is out-of-date and inaccurate.

Here’s how you can optimize your LinkedIn profile:

 

Headline

Here’s an example of a good headline for a banker:

“Aspiring Banker majored in finance specializing in forecasting and risk management best practices”.

Scrolling through most professional profiles for bankers on LinkedIn, these individuals pay little attention to the headline.

A well-optimized headline gives the recruiters reasons to click on a profile. Though you just have 120 characters to make it great and charm the recruiter.

You can include pointers on what you are trying to achieve as a banker, or include your major as a way of connecting the skills-gap. If you are an MBA degree holder, then you can reflect this on your headline along with the major.

Though here are a few things you should know about creating a headline:

  • Be professional and avoid writing words like “superstar worker”, “top performer”, etc.
  • Be discreet with your job search, don’t directly mention “looking for a job”, “unemployed”, etc.
  • Research on other professional’s headlines with a network presence.
  • Include the usage of strong adjectives/action verbs.

 

Connections

On LinkedIn, develop meaningful connections with professionals and recruiters. With little effort, you can significantly increase your number of connections.

However, having 5000+ connections is not valuable if they are irrelevant to your interests. Hence, keep your connections limited to professionals in the finance industry.

  • Connect with individuals that are relevant in the finance industry and send a personalized message along with the connection request.
  • You are most likely to get ignored if you mindlessly send out requests. Though LinkedIn advocates being active, you should derive an invitation strategy for effective network expansion.
  • Message recruiters that are hiring professionals in the finance industry and ask them for advice on how you can further optimize your profile.

 

Professional Experience

Your LinkedIn profile works as a digital resume. It should give an idea of a constructive career progression. Hence, LinkedIn profile optimization becomes quite important.

  • Write points in a bullet form, don’t include long paragraphs.
  • Mentioning your roles and responsibilities isn’t ideal. Construct the points in a way that showcase all your accomplishments & contributions.
  • Add your projects separately; do not add them in the career highlights section.

 

Keywords

As with any other search engine, recruiters are dependent on the algorithm to show them the best profile as per their searches. Based on a certain set of relevant keywords in your industry, recruiters will try to search for candidates on LinkedIn.

Here’s how you can use keywords to optimize your profile:

  • Research: Thoroughly research the keywords that are of prime importance in the finance industry. Check the profiles of other professionals on LinkedIn and refer job postings to gain an understanding of how to sprinkle these keywords in your profile.
  • Section: Utilize each section efficiently of your LinkedIn profile to showcase your contributions and achievements. Don’t just stuff your profile with contextual keywords. In the end, your profile should foremost be easily readable.
  • Industry and Skills: Update the industry in your profile and include all the skills you are familiar with. Further, you can even include skills that you are not familiar with. Let’s say you need to include “Budget Forecasting” in your profile and you have not had any real-life experience with it. You may write it as “Interested in gaining experience in budget forecasting”.

 

Skills & Recommendations

Recruiters look for professionals who can deliver, hence your profile should include the skills that are highly relevant to your targeted profile. Though in the banking industry recruiters search for general skills as well. So, make sure your profile is a match for both.

Further, just listing your expertise is not going to be enough. Get your mentors, employers, etc. to write you a stellar recommendation. If you provide credibility for your skills then it can do wonders for you.

 

Final Word

  • Just as the headline of your profile, your picture is equally important. Make sure you use a professional-looking photograph.
  • Continue to engage with your connections through comments and professional messaging.

As you are a banking professional, your profile is probably going to end up looking like all about your core competencies, However, it is important to include a few pointers about your hobbies that describe your personality as well.

 

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