Making decisions in fintech has never been easy. In a sector famous for its embrace of new technology, perhaps surprisingly more data and technology to contend with has done little to make it easier. Following a turbulent few years, marked by funding slowdowns,
regulatory changes and unpredictable markets, the sector is at a crossroads. Fintechs may have regained some momentum in 2024 but if they’re to sustain it smarter and strategic decision-making against a backdrop where change is a constant will be key.
However, while new technologies have opened up opportunities to gain fresh insights and extract more from our businesses’ information, the decision-making process has changed faster than we can. It’s rapid techceleration, yet the very technologies promising
whole new levels of agility and performance have, in many cases, heaped on layers of complexity, clouding the decision-making process.
Wading through data to find valuable insights was previously like searching for a needle in a haystack. Analytics tools and, more recently, AI are changing this situation at breakneck speed, reshaping decision making at pace. For instance,
Gartner predicts that by 2027, 90% of descriptive and diagnostic analytics in finance will be fully automated. This figure alone should prompt a real rethink for the sector about its readiness for a new autonomous finance function. The clock is ticking
and there is a real risk of losing control for those who aren’t ready.
In a world where tech moves faster than we can think, we run the risk of acting on impulse. Snap decisions can cause fragmented ways of working and reduce standardisation – ultimately resulting in operational chaos and further clouding the decision-making
process. In fact, Software AG’s recent
Reality Check Survey highlights that this operational chaos of different processes and systems slows down decision making and action for 80% of businesses, with 76% saying that their growing tech infrastructure has made work more challenging.
Decisions are increasingly being made at a tactical level, based on specific tools, instead of at a strategic level, based on overall goals. So, what’s the secret to implementing new technologies without sacrificing operational efficiency?
AI infiltrates decision making
Over time, quick technology fixes can distort the execution of the overall mission. AI tools are a perfect example: as employees stare down a rabbit hole of different tools that they can use, they get further away from any clear goal that they might have
started out with. A great example of how new technologies can impact processes and decision making is the implementation of AI.
While AI in its current form helps to alleviate mundane and manual tasks so that teams can focus on revenue generation, value-add activities, or innovation, it has also fast become an integral part of the decision making process. Able to sift through data
rapidly and offer up insights at the click of a button, LLMs and generative AI are taking a lot of the grunt work out of decisions, allowing leaders to focus on the strategic side of their roles. But fragmented and uncontrolled use, can cause issues with inefficiency,
security, and an imbalance in skills.
Bringing balance to chaos
Moving too quickly can result in technology that’s not tailored to its purpose, which can result in greater inefficiency and non-compliance than before. If that chaos slows down decision making as well, it compounds inefficiencies and employees feel forced
to find their own [unregulated] resources. Leaders hit the right balance when they take both the interests of the board and shareholders, and the reality of the those on the ground into account.
While a top-down approach can ensure accountability, strategic alignment, and standardisation, sometimes it doesn’t match employee realities. For instance, an estimated
80% of AI projects fail due to misunderstanding or miscommunication from stakeholders on which problems need to be solved using AI. Making decisions from the bottom up, balanced with standardisation and training from central management, creates a more accurate
picture of an organisation’s reality. Turning staff who are executing processes into decision makers gives businesses and employees the best of both worlds.
Creating a clear view for decision makers
Looking to the future capabilities of AI, we need to see leaders grasping the fundamentals now to avoid chaos, confusion, and falling behind competitors. Decision-makers need to grasp not just technical skills like how to train a model, but also discern
how models operate at their cores, anticipate the ripple effects of integration across functions, and tailor workflows accordingly to maximise the tools at their disposal.
As we can see, a certain level of chaos is acceptable, even key, but only if it is managed. All this starts with a clear view of operations and transparency in decision making, fuelling process intelligence.
Remember, increasing productivity within just one area of an end-to-end process can cause disruption and increases the risk of organisational chaos. Especially where end-to-end processes traverse departments and functional areas -think new employee onboarding,
product launches and customer onboarding to name a few. To mitigate the risk of increased chaos end-to-end process intelligence and visibility is an imperative.
The ability to assess which processes are highest risk or least efficient means that decision makers prioritise where to focus their efforts, and where there are acceptable levels of deviation (or chaos). Holistic insight into performance, process, and goals
can allow the organisation to “think” as a single organism, instead of an amalgam of siloed groups and processes.
This transparency also powers evidence-based decision making. Being able to explain a decision is important, but that doesn’t always mean it’s the right one. Different perspectives and expertise are essential in decision making, like AI strategies needing
AI expertise, but also the opinions of those using the technology.
Where patience meets process
Whether the goal is to create an innovative financial product, streamline compliance workflows, or simply to make core functions more efficient, the processes that fintechs put in place now will have a fundamental influence on success. Retrofitting AI into
these processes will require great time and patience but opportunity knocks for those putting in the hard yards now.
Those who rush into AI adoption, only to realise that their existing business infrastructure and long-standing processes are not built with automation in mind, will see a disruptive impact. AI can’t be seen as a silver bullet that will magically transform
decision-making.
The transition to an AI driven fintech future requires incremental changes and ongoing optimisation of core systems to ensure success. A constant feedback loop of analysis approaches can help hone in on long-term goals, while ensuring compliance with new
regulation including DORA. It also affords financial services leaders greater confidence in AI-powered decision making, safe in the knowledge that AI will fuel success in operations, risk management and critical areas like fraud detection and prevention.
Rethinking and adjusting to techceleration helps leaders create an environment where AI can flourish, innovation is nurtured and efficiency reigns. Short-term focus and a rush to jump on the latest tech trends are disruptive in a negative sense. With a structured
long-term vision, AI can underpin innovation, business impact and compliance, delivering stability. Get the fundamentals in place and make new tech an enabler to efficient operations and good decisions.
