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Simplicity Is Key To Competing In Complex Capital Markets

sougatadatta

two men discuss capital marketsThe past five years have been extremely challenging for banks and capital markets.  Basel III and Dodd-Frank have not only made things more complex, but they’ve also dealt a blow to the bottom line.  According to McKinsey & Company, Basel III and other regulatory compliance could potentially cost anywhere from 3 to 5 percent return on equity (ROE).

Global firms are faced with different regulations in every country in which they operate.  Adding to that complexity, governments are demanding greater transparency, reporting, and documentation.  And, at the same time, new CCAR stress testing is becoming more burdensome every year – what was satisfactory in 2014 may not be acceptable in 2015.

The increased scrutiny means an increase in data processing for financial services firms.  But legacy systems that aren’t synchronized have separate silos for different businesses and asset classes, and often have agonizingly slow batch-processing that make regulatory compliance difficult.  Many firms have managed to cope by throwing a lot of bodies at the problem, especially outsourced data reconciliations and clean up – a strategy that isn’t sustainable over the long haul.

All this complexity – in architecture, processing, data acquisition, integrity, and reporting – is squeezing resources available for revenue generation, and for true innovation.

Some firms have figured out that if they must expense to meet regulatory requirements, they should find a way to use their new-found access to intelligence to transform their business for the future and greater market competitiveness.

Regulatory compliance is the initial driver, but competitive advantage is the end result.

Making the next step a giant step forward for capital markets

The fact is that financial institutions today can’t afford to be encumbered by old, inflexible technology.  Those who are not innovating and adapting will fall behind, unable to satisfy customer and market demands.

But developing a golden data source that provides fast access to complete, correct, consistent data across the enterprise – the same information that’s used to report earnings, and calculate profit and loss, but also for risk and regulatory reporting – is no longer an unattainable goal.  And it doesn’t have to reside in a single database; it could be in many that function as one, with uniform governance and data models across the organization.

There are two ways to capitalize on killing complexity.  The first is to create a robust platform for high growth or expansion, in businesses, geographies and in sheer transaction volume.  The second is to use business intelligence to optimize capital allocation across legal entities, increase growth and revenue, lower operating costs, and provide better customer experiences.

The SAP HANA platform makes both possible.  When deployed in the cloud, it simplifies data architecture, and reduces latency, complexity, and cost.

Financial services companies that are reducing complexities and running simple are becoming more agile, transitioning away from their old environments and opening new opportunities for the future. Run Simple today.

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sougatadatta

About sougatadatta

Sougata Datta is the Senior Director, Digital Enterprise Platform Group - HCP, at SAP. His specialties include business intelligence, solution architecture, data warehousing, data modeling, software development and analytics.

Time For Banks To Fight Back

Laurence Leyden

Metamora, Illinois, USA --- USA, Illinois, Metamora, Close-up of man photographing checque --- Image by © Vstock LLC/Tetra Images/CorbisThe financial services industry has suffered consecutive blows in recent years. The global banking crisis, new regulations, empowered customers calling the shots, not to mention a new breed of digital disruptors out to steal market share, have wreaked havoc on business as usual.  Profits have been slashed, reputations have been damaged, and management has been blindsided.

The only way forward is change – a change of business model, a change of mindset, and a change of ecosystem.  It’s a major upheaval, and not to be taken lightly. Banks in particular have operated largely the same way for the past 300 years. Management is facing a once in a generation reassessment of 21st century banking.

Changes in customer behaviour, including 24×7 omnichannel service expectations, lack of loyalty by current customers willing to exchange privacy for easier access to information, generational expectations of future customers – “screenagers” and tech savvy Millennials – and technology advances in cloud, mobile, real-time data, and predictive analytics make yesterday’s business model redundant.

Banking isn’t actually about banking anymore. It’s about enabling people’s lifestyles. That means you have to completely re-think how you engage with customers. The lessons are everywhere in parallel industries. Nokia, for example, thought it was about the phone, not the customer experience. Digitisation has both emboldened and empowered customers. Ignoring this fact is pointless. You need to cater to what consumers want. That means your back-end systems need to be integrated, consistent, contextualised and easy to deploy across any channel.

There’s also a whole new ecosystem required to support this new business model. Banks are facing disaggregation as they no longer own the end-to-end value chain, as well as disintermediation as new market entrants attack specific parts of the business (think Apple Pay). Smart banks are forging relationships with different and unexpected partners, such as mobile and retail organisations, even providing products from outside of the group where they are the best fit for a customer’s needs.  As I’ve said in one of my previous blogs, there’s a new mantra for modern banking: “Must play well with others.”

Old-fashioned banking is gone, and with it so have old style processes, business models and attitudes. Nobody wants to be the last dinosaur.  It’s time for the industry to dust itself off, and step up. Embracing change is easier – and far more profitable – than risking irrelevance in the widening digital divide.

I’ve briefly summarised only some of the key drivers of digital transformation, but you can find much more insight – including views from thought leaders in banks, insurance companies, fintech providers, challenger banks and aggregators – by downloading the eBook from the recent SAP Financial Services Forum: The digital evolution – As technology transforms financial services who will triumph.

It’s essential reading if you’re going to successfully fight back.

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Laurence Leyden

About Laurence Leyden

Laurence is general manager of Financial Services, EMEA, at SAP and is primarily involved in helping banks in their transformation agenda. Prior to SAP he worked for numerous banks in Europe and Asia including Barclays, Lloyds Banking Group and HSBC. He regularly presents on industry trends and SAP’s banking strategy.

Why Banks Should Be Bullish On Integrating Finance And Risk Data

Mike Russo

Welcome to the regulatory world of banking, where finance and risk must join forces to banking executiveensure compliance and control. Today it’s no longer sufficient to manage your bank’s performance using finance-only metrics such as net income. What you need is a risk-adjusted view of performance that identifies how much revenue you earn relative to the amount of risk you take on. That requires metrics that combine finance and risk components, such as risk-adjusted return on capital, shareholder value added, or economic value added.

While the smart money is on a unified approach to finance and risk, most banking institutions have isolated each function in a discrete technology “silo” complete with its own data set, models, applications, and reporting components. What’s more, banks continually reuse and replicate their finance and risk-related data – resulting in the creation of additional data stores filled with redundant data that grows exponentially over time. Integrating all this data on a single platform that supports both finance and risk scenarios can provide the data integrity and insight needed to meet regulations. Such an initiative may involve some heavy lifting, but the advantages extend far beyond compliance.

Cashing in on bottom-line benefits

Consider the potential cost savings of taking a more holistic approach to data management. In our work with large global banks, we estimate that data management – including validation, reconciliation, and copying data from one data mart to another – accounts for 50% to 70% of total IT costs. Now factor in the benefits of reining in redundancy. One bank we’re currently working with is storing the same finance and risk-related data 20 times. This represents a huge opportunity to save costs by eliminating data redundancy and all the associated processes that unfold once you start replicating data across multiple sources.

With the convergence of finance and risk, we’re seeing more banks reviewing their data architecture, thinking about new models, and considering how to handle data in a smarter way. Thanks to modern methodologies, building a unified platform that aligns finance and risk no longer requires a rip-and-replace process that can disrupt operations. As with any enterprise initiative, it’s best to take a phased approach.

Best practices in creating a unified data platform

Start by identifying a chief data officer (CDO) who has strategic responsibility for the unified platform, including data governance, quality, architecture, and analytics. The CDO oversees the initiative, represents all constituencies, and ensures that the new data architecture serves the interests of all stakeholders.

Next, define a unified set of terms that satisfies both your finance and risk constituencies while addressing regulatory requirements. This creates a common language across the enterprise so all stakeholders clearly understand what the data means. Make sure all stakeholders have an opportunity to weigh in and explain their perspective of the data early on because certain terms can mean different things to finance and risk folks.

In designing your platform, take advantage of new technologies that make previous IT models predicated on compute-intensive risk modeling a thing of the past. For example, in-memory computing now enables you to integrate all information and analytic processes in memory, so you can perform calculations on-the-fly and deliver results in real time. Advanced event stream processing lets you run analytics against transaction data as it’s posting, so you can analyze and act on events as they happen.

Such technologies bring integration, speed, flexibility, and access to finance and risk data. They eliminate the need to move data to data marts and reconcile data to meet user requirements. Now a single finance and risk data warehouse can be flexible and comprehensive enough to serve many masters.

Join our webinar with Risk.net on 7 October, 2015 to learn best practices and benefits of deploying an integrated finance and risk platform.

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Mike Russo

About Mike Russo

Mike Russo is senior industry principal, Financial Services, with SAP. Mike has 30 years of experience in the financial services/financial software industries. This includes stints as senior auditor for the Irving Trust Co., New York; manager of the International Department at Barclays Bank of New York; and 14 years as CFO for Nordea Bank’s New York City branch – a full-service retail/commercial bank. Mike also served on Nordea’s Credit, IT, and Risk Committees. Mike’s financial software experience includes roles as a senior banking consultant with Sanchez Computer Associates and manager of Global Business Solutions (focused on sale of financial/risk management solutions) with Thomson Financial. Before joining SAP, Mike was a regulator with the Federal Reserve Bank in Charlotte, where he was responsible for the supervision of large commercial banking organizations in the Southeast with a focus on market/credit/operational risk management.

How Emotionally Aware Computing Can Bring Happiness to Your Organization

Christopher Koch


Do you feel me?

Just as once-novel voice recognition technology is now a ubiquitous part of human–machine relationships, so too could mood recognition technology (aka “affective computing”) soon pervade digital interactions.

Through the application of machine learning, Big Data inputs, image recognition, sensors, and in some cases robotics, artificially intelligent systems hunt for affective clues: widened eyes, quickened speech, and crossed arms, as well as heart rate or skin changes.




Emotions are big business

The global affective computing market is estimated to grow from just over US$9.3 billion a year in 2015 to more than $42.5 billion by 2020.

Source: “Affective Computing Market 2015 – Technology, Software, Hardware, Vertical, & Regional Forecasts to 2020 for the $42 Billion Industry” (Research and Markets, 2015)

Customer experience is the sweet spot

Forrester found that emotion was the number-one factor in determining customer loyalty in 17 out of the 18 industries it surveyed – far more important than the ease or effectiveness of customers’ interactions with a company.


Source: “You Can’t Afford to Overlook Your Customers’ Emotional Experience” (Forrester, 2015)


Humana gets an emotional clue

Source: “Artificial Intelligence Helps Humana Avoid Call Center Meltdowns” (The Wall Street Journal, October 27, 2016)

Insurer Humana uses artificial intelligence software that can detect conversational cues to guide call-center workers through difficult customer calls. The system recognizes that a steady rise in the pitch of a customer’s voice or instances of agent and customer talking over one another are causes for concern.

The system has led to hard results: Humana says it has seen an 28% improvement in customer satisfaction, a 63% improvement in agent engagement, and a 6% improvement in first-contact resolution.


Spread happiness across the organization

Source: “Happiness and Productivity” (University of Warwick, February 10, 2014)

Employers could monitor employee moods to make organizational adjustments that increase productivity, effectiveness, and satisfaction. Happy employees are around 12% more productive.




Walking on emotional eggshells

Whether customers and employees will be comfortable having their emotions logged and broadcast by companies is an open question. Customers may find some uses of affective computing creepy or, worse, predatory. Be sure to get their permission.


Other limiting factors

The availability of the data required to infer a person’s emotional state is still limited. Further, it can be difficult to capture all the physical cues that may be relevant to an interaction, such as facial expression, tone of voice, or posture.



Get a head start


Discover the data

Companies should determine what inferences about mental states they want the system to make and how accurately those inferences can be made using the inputs available.


Work with IT

Involve IT and engineering groups to figure out the challenges of integrating with existing systems for collecting, assimilating, and analyzing large volumes of emotional data.


Consider the complexity

Some emotions may be more difficult to discern or respond to. Context is also key. An emotionally aware machine would need to respond differently to frustration in a user in an educational setting than to frustration in a user in a vehicle.

 


 

download arrowTo learn more about how affective computing can help your organization, read the feature story Empathy: The Killer App for Artificial Intelligence.


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Christopher Koch

About Christopher Koch

Christopher Koch is the Editorial Director of the SAP Center for Business Insight. He is an experienced publishing professional, researcher, editor, and writer in business, technology, and B2B marketing. Share your thoughts with Chris on Twitter @Ckochster.

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In An Agile Environment, Revenue Models Are Flexible Too

Todd Wasserman

In 2012, Dollar Shave Club burst on the scene with a cheeky viral video that won praise for its creativity and marketing acumen. Less heralded at the time was the startup’s pricing model, which swapped traditional retail for subscriptions.

For as low as $1 a month (for five two-bladed cartridges), consumers got a package in the mail that saved them a trip to the pharmacy or grocery store. Dollar Shave Club received the ultimate vindication for the idea in 2016 when Unilever purchased the company for $1 billion.

As that example shows, new technology creates the possibility for new pricing models that can disrupt existing industries. The same phenomenon has occurred in software, in which the cloud and Web-based interfaces have ushered in Software as a Service (SaaS), which charges users on a monthly basis, like a utility, instead of the typical purchase-and-later-upgrade model.

Pricing, in other words, is a variable that can be used to disrupt industries. Other options include usage-based pricing and freemium.

Products as services, services as products

There are basically two ways that businesses can use pricing to disrupt the status quo: Turn products into services and turn services into products. Dollar Shave Club and SaaS are two examples of turning products into services.

Others include Amazon’s Dash, a bare-bones Internet of Things device that lets consumers reorder items ranging from Campbell’s Soup to Play-Doh. Another example is Rent the Runway, which rents high-end fashion items for a weekend rather than selling the items. Trunk Club offers a twist on this by sending items picked out by a stylist to users every month. Users pay for what they want and send back the rest.

The other option is productizing a service. Restaurant franchising is based on this model. While the restaurant offers food service to consumers, for entrepreneurs the franchise offers guidance and brand equity that can be condensed into a product format. For instance, a global HR firm called Littler has productized its offerings with Littler CaseSmart-Charges, which is designed for in-house attorneys and features software, project management tools, and access to flextime attorneys.

As that example shows, technology offers opportunities to try new revenue models. Another example is APIs, which have become a large source of revenue for companies. The monetization of APIs is often viewed as a side business that encompasses a wholly different pricing model that’s often engineered to create huge user bases with volume discounts.

Not a new idea

Though technology has opened up new vistas for businesses seeking alternate pricing models, Rajkumar Venkatesan, a marketing professor at University of Virginia’s Darden School of Business, points out that this isn’t necessarily a new idea. For instance, King Gillette made his fortune in the early part of the 20th Century by realizing that a cheap shaving device would pave the way for a recurring revenue stream via replacement razor blades.

“The new variation was the Keurig,” said Venkatesan, referring to the coffee machine that relies on replaceable cartridges. “It has started becoming more prevalent in the last 10 years, but the fundamental model has been there.” For businesses, this can be an attractive model not only for the recurring revenue but also for the ability to cross-sell new goods to existing customers, Venkatesan said.

Another benefit to a subscription model is that it can also supply first-party data that companies can use to better understand and market to their customers. Some believe that Dollar Shave Club’s close relationship with its young male user base was one reason for Unilever’s purchase, for instance. In such a cut-throat market, such relationships can fetch a high price.

To learn more about how you can monetize disruption, watch this video overview of the new SAP Hybris Revenue Cloud.

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