Complexity Reduction

One of the key challenges to a commercial growth agenda in financial services and one of the most relevant drivers of rising cost to run operations is complexity. Whereas cost can always be reduced and managed to budget, often a fundamental re-thinking of wanted and unwanted internal dependencies of actions in a corporation is required to ensure viable growth and future stability.

1. KEY TRENDS CURRENTL AT PLAY

In the past 3 years, fundamentals have been re-shaped by poly-crisis and a polycentric world, outlier events like war, inflation, the aftermath of a global pandemic and mass migration are becoming the new normal. Singularity events (i.e., single points of failure such as the Crowdstrike incident) as well as step changes in technology (such as OpenAI and DeepSeek) are redrawing the general assumptions of operational boundaries. We are on fact looking at a different context:

·????? Extremity: Risk models, products and simulations for extreme events (including market swings) are becoming realistic rather than statistical outliers

·????? Resilience: Organizations are facing need to move staff from permanent crisis/reaction mode to resilient mental models capable of delivering answers

·????? Location: Supply chain-like thinking in IT/Ops leading to an “optionality view” on locations and vendor strategies in a poly-centric and multi-local world

·????? Black swan reloaded: Singularities (single events with large impact) requiring approaches to exploit and prevent exposures.

The answer to this changed world view lies in a set of beliefs to which complexity reduction is central. First, business value creation has to be the focus of operating models. Second, production factors need to be viewed as supply and demand where supply has to become de-coupled from demand via business service catalogues and technical services. Talent pools need to become more flexible, also accounting for age/skill mix. And lastly, a “factory” approach to internal processes leads to standardization of key activities (e.g., fund accounting, AML/KYC, claims).

All of these four beliefs require a reduction of internal complexity.

2.??? STRATEGIC, TACTICAL AND OPERATIVE CORNERSTONES

Any approach focused on reducing complexity as a step stone to improved scalability, efficiencies and service quality has to have a commercial agenda in mind.

Strategic cornerstones

The fundamental design imperative takes a view along the value chain, working its way backwards from the customer/client. The following 5 cornerstones haven proven indispensable – sustainable growth in top line and profitability will require all of them in play:

a)?? Products have to be simple and focused: Retail customers and B2B clients alike engage with providers on clear and transparent products with a competitive pricing.

b)?? Process/service excellence constitutes a truly differentiating value proposition: Retail customer and B2B client loyalty and thus growth is driven by a superior experience at moments of truth: e.g., closing the contract (“buying the product”), onboarding, service request, and claims/payouts.

c)??? Generative AI is a game changer and unique opportunity but you have to walk first, then run: There is a lot of potential in a technology paradigm of “zero human touch” by basing all processes on generative AI, yet most enterprises will still be at a state where the foundations need to be laid (i.e., cloud approach, process simplification, classification/prioritization logic) before Generative AI can be deployed, hence the rule is: “automate to generate.”

d)?? Light infrastructure and service provider strategy de-couples unit-cost from local market: Via a “cloud first, digital first approach” and external partnerships, the approach should not carry and certainly should not focus on legacy and architectural debt. New – potentially cost-effective – options have arisen with re-architecting (re-building solutions in new code) and re-implementing (re-furbishing existing solutions with partial upgrades). Yet firstly, the management attention should lie on creating optionality by a flexibility of providers and capacity steering (doability).

e)?? Full ownership of technology is not required (“service as a service”, SaaS):? The overall ambition is to minimize human touchpoints to the entire customer journey and processes; the ambition is to maximize standardization of service activities and to drive automation of core processes. For this agenda to succeed, external providers can be leveraged while it is vital to keep full ownership of 5 value-defining levers: (1) Technology (enterprise) architecture (“cloud first”), (2) Product development/design, (3) Design of (automated) services and processes, e.g., especially claims as differentiating value proposition, (4) End-customer/client contact along the value chain (except for scheduling of service appointments via third-parties, e.g., repair shops), (5) Resulting from end-customer/client contact, all productive data.

Tactical cornerstones

The challenge of designing and executing an implementation engine is to translate the strategic headlines into max. 3 measurable objectives (“objectives and key results, OKR”). There is a built-in superiority of a “digital first” approach by assuming that any activity should be as digital as possible. However, this superiority will constantly stand in trade-offs with ad hoc events and investment challenges.

Therefore, the tactical cornerstones need to be tied together by the theme of IT/Ops convergence and simplification, a starter set is:

a)?? OKR #1: Reduce number of products. End-customers (retail) and clients (B2B) like transparent products with clear pricing and value propositions. Pricing of extra services has to be supported by a real product approach, so price increases are generally preferable, if justifiable, to introducing net new products.

b)?? OKR #2: Improve service excellence at competitive price. With regard to operations processes/activities, the objective is to focus on the simplification and digitization of operations (in this context, often referred to “end-to-end” which points out to remove break-points and run simple un-interrupted strings of activities). Service excellence goes further than meeting service level agreements and means a design of processes radically following of a “digital first” philosophy, esp. at “moments of truth.”

c)??? OKR #3: Automate rapidly with small use cases before investing in GenAI. The road to productive generative AI has three broad steps: simplification/definition of outcomes, automation, and then generative AI.

Step 1: Simplification/definition of outcomes: Define exact service steps/rules and service levels along the entire value chain, whenever an engagement with the customer (retail) or client (B2B) is involved. Service levels and concrete activities have to be described and translated into decision trees.

Step 2: Automation: This step has the objective to deliver impact rapidly, e.g. in 3 months, but involves more than simple personnel cost reduction. Build or secure access to an environment with cloud computing and the relevant tool chain, and clarify data privacy and storage solutions. In this environment, standard software providers. Focus on impactful processes first, not legacy.

Step 3: Generative AI with a specialized training data set: Many an initiative has come to grief when trying to deliver a solid ROI in one budget year. Chunk down the long list of use cases by starting with the automated processes and evolve the classification logic, e.g., by introducing co-piloting or decision proposals (e.g., in AML/KYC generated risk classifications, claims processing). Pre-trained models will be sufficient to cover level one (basic requests) and level two (standardized requests tailored to profiles) decisions.

Operational cornerstones

How to make it stick? The operative translation of tactics into day-to-day management uses regular mechanisms (reviews, management letters) to measure progress and prepare and take implementation decisions (e.g., simplification progress, documentation).

3.??? COMMON PITFALLS AND MITIGATIONS FROM PREVIOUS EFFORTS

What can be learned from previous failures and successes? The following starter list summarizes some of the most frequently observed pitfalls:

a)?? Lack of alignment: It is critical to share the same objectives across IT and Operations, and involve the senior teams early on (e.g., stability in BAU vs. change); people and their energy for cooperation create new ideas and optimize processes.

b)?? Transparent reporting and honest debate: From the start, initiatives have to be assigned to clear owners with clear scope, budget and milestones; budget has to be linked to milestones and milestones have to express outcomes.

c)??? Check of assumptions: Regular check of assumptions, especially if either progress or impact gets stuck or behind schedule (“what facts have we learned what we didn’t know when we set the ambition”).

d)?? Investment compression: Cashflow considerations and balancing of operational needs (e.g., cyber, infrastructure, sales support) can lead to an underinvestment in automation cases and a resorting to sweet rapid impact by personnel cost reductions “to meet the budget.”

e)?? Changing people is easy, building teams is hard: It is always better to have any team than a “star team”, as even teams with average performing individuals can lift substantial turnarounds and efficiency improvements if they are developed, trained and led with positive energy and a rhythm of listening, adapting and persisting.

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